{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\au206393\OneDrive - Aarhus universitet\Desktop\PSRM acceptance log-file\trait_preferences_Ukraine.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 6 May 2025, 14:11:31
{txt}
{com}. 
. 
. **************************************************************************************************************************************************
. *************************************************************** RECODINGS ************************************************************************
. **************************************************************************************************************************************************
. 
. ***************************************************************** Wave 1 *************************************************************************
. 
. ************************************************** Demographic background variables **************************************************************
. * Sex
. recode w1_q3 (1=0 "Male") (2=1 "Female") (3=.), gen(sex)
{txt}(1,081 differences between {bf:w1_q3} and {bf:sex})

{com}. 
. * Age
. clonevar age = w1_q4
{txt}
{com}. 
. * Education
. recode w1_q6 (1 2 =1 "Primary or High school") (3=2 "Professional-technical (vocational)") (4=3 "Incomplete higher") (5=4 "Bachelor degree") (6 7=5 "Master degree & Doctorate") (8=.), gen(education)
{txt}(1,048 differences between {bf:w1_q6} and {bf:education})

{com}. tab education

    {txt}RECODE of w1_q6 (6. What is the {c |}
highest level of education that you {c |}
                       have complet {c |}      Freq.     Percent        Cum.
{hline 36}{c +}{hline 35}
             Primary or High school {c |}{res}         95        8.86        8.86
{txt}Professional-technical (vocational) {c |}{res}        185       17.26       26.12
{txt}                  Incomplete higher {c |}{res}         85        7.93       34.05
{txt}                    Bachelor degree {c |}{res}        188       17.54       51.59
{txt}          Master degree & Doctorate {c |}{res}        519       48.41      100.00
{txt}{hline 36}{c +}{hline 35}
                              Total {c |}{res}      1,072      100.00
{txt}
{com}. 
. * Region
. clonevar region = w1_region_aggregate
{txt}
{com}. 
. 
. ************************************************ Experimental treatment for Ideal Leader Experiment **********************************************
. * Experimental treatment for leader trait evaluation questions
. recode w1_leader_exp_condition (1=1 "Conflict, now") (2=2 "Peace, future"), generate(Context)
{txt}(0 differences between {bf:w1_leader_exp_condition} and {bf:Context})

{com}. 
. clonevar Conflict_1 = Context
{txt}
{com}. 
. 
. *************************************** Leadership trait preferences in IDEAL LEADER *************************************************************
. * Competent
. recode w1_q14_1 (8=.)
{txt}(57 changes made to {bf:w1_q14_1})

{com}. generate Competence_1 = (w1_q14_1-1)/6
{txt}(57 missing values generated)

{com}. 
. * Trustworthy
. recode w1_q14_2 (8=.)
{txt}(36 changes made to {bf:w1_q14_2})

{com}. generate Trustworthy_1 = (w1_q14_2-1)/6
{txt}(36 missing values generated)

{com}. 
. * Dominant
. recode w1_q14_3 (8=.)
{txt}(44 changes made to {bf:w1_q14_3})

{com}. generate Dominant_1 = (w1_q14_3-1)/6
{txt}(44 missing values generated)

{com}. 
. * Generous
. recode w1_q14_4 (8=.)
{txt}(36 changes made to {bf:w1_q14_4})

{com}. generate Generous_1 = (w1_q14_4-1)/6
{txt}(36 missing values generated)

{com}. 
. * Strong
. recode w1_q14_5 (8=.)
{txt}(26 changes made to {bf:w1_q14_5})

{com}. generate Strong_1 = (w1_q14_5-1)/6
{txt}(26 missing values generated)

{com}. 
. * Warm
. recode w1_q14_6 (8=.)
{txt}(36 changes made to {bf:w1_q14_6})

{com}. generate Warm_1 = (w1_q14_6-1)/6
{txt}(36 missing values generated)

{com}. 
. * Tough-minded
. recode w1_q14_7 (8=.)
{txt}(39 changes made to {bf:w1_q14_7})

{com}. generate Toughminded_1 = (w1_q14_7-1)/6
{txt}(39 missing values generated)

{com}. 
. summ Competence_1 Trustworthy_1 Dominant_1 Generous_1 Strong_1 Warm_1 Toughminded_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Competence_1 {c |}{res}      1,024    .8802083    .1955989          0          1
{txt}Trustworth~1 {c |}{res}      1,045    .9197767    .1490432          0          1
{txt}{space 2}Dominant_1 {c |}{res}      1,037    .5940212    .2986719          0          1
{txt}{space 2}Generous_1 {c |}{res}      1,045    .7279107    .2446149          0          1
{txt}{space 4}Strong_1 {c |}{res}      1,055    .8840442    .1613094          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}Warm_1 {c |}{res}      1,045    .7090909    .2508124          0          1
{txt}Toughminde~1 {c |}{res}      1,042    .4328215    .2978518          0          1
{txt}
{com}. 
. 
. *** Exploring dimensions in trait impressions of IDEAL LEADER based on Principal Component Analysis
. factor Competence_1 Trustworthy_1 Dominant_1 Generous_1 Strong_1 Warm_1 Toughminded_1, pcf
{txt}(obs=988)

Factor analysis/correlation{col 50}Number of obs    = {res}       988
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.94691      1.63211            0.4210       0.4210
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.31480      0.15262            0.1878       0.6088
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.16218      0.64733            0.1660       0.7748
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.51485      0.07283            0.0736       0.8484
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.44202      0.11587            0.0631       0.9115
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.32615      0.03306            0.0466       0.9581
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.29309            .            0.0419       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 2287.98{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7244}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1156}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4688}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7713}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0784}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4435}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2023}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5129}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3071}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2550}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6790}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3166}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5285}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1593}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7863}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0207}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2619}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3127}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6192}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3805}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5378}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1825}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3185}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8138}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1195}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2220}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate, oblique oblimin

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       988
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: oblique oblimin (Kaiser off){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |}     Variance   Proportion    Rotated factors are correlated
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.52111       0.3602
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.07562       0.2965
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.65819       0.2369
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 2287.98{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8948}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0361}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0660}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8997}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0110}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2023}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0012}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1889}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8147}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2550}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0321}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8991}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0337}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1593}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7419}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1038}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1418}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3127}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0077}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.9118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0395}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1825}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0112}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1479}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8895}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2220}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8723}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.7067}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.4645}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.0817}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.3812}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8526}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.4822}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5961}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.2395}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. 
. * Creates factor score variables for robustness tests of main results
. predict Comp_PCA_1 Warm_PCA_1 Domi_PCA_1
{txt}(option {bf:regression} assumed; regression scoring)

{p 0 0 2}Scoring coefficients (method = regression; based on oblimin(0) rotated factors){p_end}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.41606}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.03323}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.05736}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.41715}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.01981}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.02064}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.01427}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.10008}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.54781}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.00139}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.52569}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.01066}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.34010}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.04823}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.08340}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.01619}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.53466}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.03833}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00586}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.09827}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.60254}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}


{com}. corr Comp_PCA_1 Warm_PCA_1 Domi_PCA_1
{txt}(obs=988)

             {c |} Comp_P~1 Warm_P~1 Domi_P~1
{hline 13}{c +}{hline 27}
  Comp_PCA_1 {c |}{res}   1.0000
  {txt}Warm_PCA_1 {c |}{res}   0.3601   1.0000
  {txt}Domi_PCA_1 {c |}{res}   0.2201   0.1460   1.0000

{txt}
{com}. 
. *** Main outcome variables: Composite scales for dominance, warmth and competence (on 0-1 scales)
. egen Domi_scale_1 = rowmean(Dominant_1 Toughminded_1)
{txt}(30 missing values generated)

{com}. 
. egen Comp_scale_1 = rowmean(Competence_1 Trustworthy_1 Strong_1)
{txt}(24 missing values generated)

{com}. 
. egen Warm_scale_1 = rowmean(Warm_1 Generous_1)
{txt}(26 missing values generated)

{com}. 
. summ Domi_scale_1 Comp_scale_1 Warm_scale_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale_1 {c |}{res}      1,051     .512052    .2588131          0          1
{txt}Comp_scale_1 {c |}{res}      1,057    .8936718     .145519          0          1
{txt}Warm_scale_1 {c |}{res}      1,055    .7185624    .2263167          0          1
{txt}
{com}. corr Domi_scale_1 Comp_scale_1 Warm_scale_1
{txt}(obs=1,044)

             {c |} Domi_s~1 Comp_s~1 Warm_s~1
{hline 13}{c +}{hline 27}
Domi_scale_1 {c |}{res}   1.0000
{txt}Comp_scale_1 {c |}{res}   0.2295   1.0000
{txt}Warm_scale_1 {c |}{res}   0.1570   0.3740   1.0000

{txt}
{com}. 
. 
. ****************************************** Leadership trait perceptions of CURRENT LEADER, Zelenskyy *********************************************
. * Competent
. recode w1_q15_1 (8=.)
{txt}(23 changes made to {bf:w1_q15_1})

{com}. generate Zel_Comp_1 = (w1_q15_1-1)/6
{txt}(23 missing values generated)

{com}. 
. * Trustworthy
. recode w1_q15_2 (8=.)
{txt}(23 changes made to {bf:w1_q15_2})

{com}. generate Zel_Trust_1 = (w1_q15_2-1)/6
{txt}(23 missing values generated)

{com}. 
. * Dominant
. recode w1_q15_3 (8=.)
{txt}(42 changes made to {bf:w1_q15_3})

{com}. generate Zel_Domi_1 = (w1_q15_3-1)/6
{txt}(42 missing values generated)

{com}. 
. * Generous
. recode w1_q15_4 (8=.)
{txt}(38 changes made to {bf:w1_q15_4})

{com}. generate Zel_Generous_1 = (w1_q15_4-1)/6
{txt}(38 missing values generated)

{com}. 
. * Strong
. recode w1_q15_5 (8=.)
{txt}(22 changes made to {bf:w1_q15_5})

{com}. generate Zel_Strong_1 = (w1_q15_5-1)/6
{txt}(22 missing values generated)

{com}. 
. * Warm
. recode w1_q15_6 (8=.)
{txt}(34 changes made to {bf:w1_q15_6})

{com}. generate Zel_Warm_1 = (w1_q15_6-1)/6
{txt}(34 missing values generated)

{com}. 
. * Tough-minded
. recode w1_q15_7 (8=.)
{txt}(43 changes made to {bf:w1_q15_7})

{com}. generate Zel_Tough_1 = (w1_q15_7-1)/6
{txt}(43 missing values generated)

{com}. 
. 
. summ Zel_Comp_1 Zel_Trust_1 Zel_Domi_1 Zel_Generous_1 Zel_Strong_1 Zel_Warm_1 Zel_Tough_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}Zel_Comp_1 {c |}{res}      1,058    .7463768    .3055257          0          1
{txt}{space 1}Zel_Trust_1 {c |}{res}      1,058    .7840265    .2991983          0          1
{txt}{space 2}Zel_Domi_1 {c |}{res}      1,039     .563683    .3329842          0          1
{txt}Zel_Genero~1 {c |}{res}      1,043    .7102908    .3076971          0          1
{txt}Zel_Strong_1 {c |}{res}      1,059    .7610954    .3130257          0          1
{txt}{hline 13}{c +}{hline 57}
{space 2}Zel_Warm_1 {c |}{res}      1,047    .7184018    .3060069          0          1
{txt}{space 1}Zel_Tough_1 {c |}{res}      1,038    .3908157    .3077853          0          1
{txt}
{com}. 
. 
. ** Creates composite scales for perceptions of Zelenskyy on the same three trait dimensions as for "ideal leader ratings": dominance, warmth and competence.
. egen Comp_scale_Zel1 = rowmean(Zel_Comp_1 Zel_Trust_1 Zel_Strong_1)
{txt}(18 missing values generated)

{com}. alpha Zel_Comp_1 Zel_Trust_1 Zel_Strong_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0798795
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.9458
{txt}
{com}. 
. egen Warm_scale_Zel1 = rowmean(Zel_Warm_1 Zel_Generous_1)
{txt}(27 missing values generated)

{com}. alpha Zel_Warm_1 Zel_Generous_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0786946
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.9105
{txt}
{com}. 
. egen Domi_scale_Zel1= rowmean(Zel_Domi_1 Zel_Tough_1)
{txt}(29 missing values generated)

{com}. alpha Zel_Domi_1 Zel_Tough_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .050889
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.6622
{txt}
{com}. 
. summ Comp_scale_Zel1 Warm_scale_Zel1 Domi_scale_Zel1 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scal~l1 {c |}{res}      1,063    .7637713    .2908337          0          1
{txt}Warm_scal~l1 {c |}{res}      1,054    .7145003    .2931831          0          1
{txt}Domi_scal~l1 {c |}{res}      1,052    .4771863    .2808222          0          1
{txt}
{com}. 
. 
. 
. 
. 
. ****************************************************** Self-reported emotional reactions over last week ******************************************
. * Afraid
. recode w1_q11_1 (8=.), generate(afraid_1)
{txt}(18 differences between {bf:w1_q11_1} and {bf:afraid_1})

{com}. * Frightened
. recode w1_q11_2 (8=.), generate(frightened_1)
{txt}(14 differences between {bf:w1_q11_2} and {bf:frightened_1})

{com}. * Scared
. recode w1_q11_3 (8=.), generate(scared_1)
{txt}(20 differences between {bf:w1_q11_3} and {bf:scared_1})

{com}. 
. ** Composite scale for anxiety
. corr afraid_1 frightened_1 scared_1
{txt}(obs=1,056)

             {c |} afraid_1 fright~1 scared_1
{hline 13}{c +}{hline 27}
    afraid_1 {c |}{res}   1.0000
{txt}frightened_1 {c |}{res}   0.8053   1.0000
    {txt}scared_1 {c |}{res}   0.7059   0.7202   1.0000

{txt}
{com}. alpha afraid_1 frightened_1 scared_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  2.42818
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8956
{txt}
{com}. egen fearfull_scale_W1_7 = rowmean(afraid_1 frightened_1 scared_1)
{txt}(11 missing values generated)

{com}. generate fearfull_scale_1 = (fearfull_scale_W1_7-1)/6
{txt}(11 missing values generated)

{com}. 
. * Angry
. recode w1_q11_4 (8=.), generate(angry_1)
{txt}(17 differences between {bf:w1_q11_4} and {bf:angry_1})

{com}. * Hostile
. recode w1_q11_5 (8=.), generate(hostile_1)
{txt}(37 differences between {bf:w1_q11_5} and {bf:hostile_1})

{com}. * Disgusted
. recode w1_q11_6 (8=.), generate(disgusted_1)
{txt}(37 differences between {bf:w1_q11_6} and {bf:disgusted_1})

{com}. 
. ** Composite scale for agressive emotions
. corr angry_1 hostile_1 disgusted_1
{txt}(obs=1,021)

             {c |}  angry_1 hostil~1 disgus~1
{hline 13}{c +}{hline 27}
     angry_1 {c |}{res}   1.0000
   {txt}hostile_1 {c |}{res}   0.5608   1.0000
 {txt}disgusted_1 {c |}{res}   0.5519   0.4918   1.0000

{txt}
{com}. alpha angry_1 hostile_1 disgusted_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.540405
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7685
{txt}
{com}. egen aggressive_scale_W1_7 = rowmean(angry_1 hostile_1 disgusted_1)
{txt}(11 missing values generated)

{com}. generate aggressive_scale_1 = (aggressive_scale_W1_7-1)/6
{txt}(11 missing values generated)

{com}. 
. * Sad
. recode w1_q11_7 (8=.), generate(sad_1)
{txt}(11 differences between {bf:w1_q11_7} and {bf:sad_1})

{com}. * Lonely
. recode w1_q11_8 (8=.), generate(lonely_1)
{txt}(23 differences between {bf:w1_q11_8} and {bf:lonely_1})

{com}. * Downhearted
. recode w1_q11_9 (8=.), generate(downhearted_1)
{txt}(16 differences between {bf:w1_q11_9} and {bf:downhearted_1})

{com}. 
. ** Composite scale for sadness
. corr sad_1 lonely_1 downhearted_1 
{txt}(obs=1,054)

             {c |}    sad_1 lonely_1 downhe~1
{hline 13}{c +}{hline 27}
       sad_1 {c |}{res}   1.0000
    {txt}lonely_1 {c |}{res}   0.3668   1.0000
{txt}downhearte~1 {c |}{res}   0.6018   0.4300   1.0000

{txt}
{com}. alpha sad_1 lonely_1 downhearted_1 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.410318
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7230
{txt}
{com}. egen sadness_scale_W1_7 = rowmean(sad_1 lonely_1 downhearted_1)
{txt}(7 missing values generated)

{com}. generate sadness_scale_1 = (sadness_scale_W1_7-1)/6
{txt}(7 missing values generated)

{com}. 
. * Proud
. recode w1_q11_10 (8=.), generate(proud_1)
{txt}(26 differences between {bf:w1_q11_10} and {bf:proud_1})

{com}. * Strong
. recode w1_q11_11 (8=.), generate(strong_1)
{txt}(23 differences between {bf:w1_q11_11} and {bf:strong_1})

{com}. * Confident
. recode w1_q11_12 (8=.), generate(confident_1)
{txt}(18 differences between {bf:w1_q11_12} and {bf:confident_1})

{com}. 
. ** Composite scale for self-confident emotions
. corr proud_1 strong_1 confident_1
{txt}(obs=1,041)

             {c |}  proud_1 strong_1 confid~1
{hline 13}{c +}{hline 27}
     proud_1 {c |}{res}   1.0000
    {txt}strong_1 {c |}{res}   0.5553   1.0000
 {txt}confident_1 {c |}{res}   0.4869   0.6624   1.0000

{txt}
{com}. alpha proud_1 strong_1 confident_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.484637
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7922
{txt}
{com}. egen selfconf_scale_W1_7 = rowmean(proud_1 strong_1 confident_1)
{txt}(11 missing values generated)

{com}. generate selfconf_scale_1 = (selfconf_scale_W1_7-1)/6
{txt}(11 missing values generated)

{com}. 
. summ fearfull_scale_1 aggressive_scale_1 sadness_scale_1 selfconf_scale_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~1 {c |}{res}      1,070    .4757529    .2744772          0          1
{txt}aggressive~1 {c |}{res}      1,070    .6362669    .2370329          0          1
{txt}sadness_sc~1 {c |}{res}      1,074    .5127767    .2336689          0          1
{txt}selfconf_s~1 {c |}{res}      1,070    .6069574    .2299484          0          1
{txt}
{com}. 
. 
. 
. 
. 
. ******************************************** Self-reported Victimization of Russian Attacks ******************************************************
. **** Inspects all three items
. codebook w1_q9_1 w1_q9_2 w1_q9_3

{txt}{hline}
{res}w1_q9_1{right:9.1 How often: The invading Russian or pro-Russian forces have directly attacked}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels11_wave1}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}0{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       803{col 32}       1{col 42}{txt}Never
{col 20}{res}        81{col 32}       2{col 42}{txt}Once
{col 20}{res}        57{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        28{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}        44{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        68{col 32}       6{col 42}{txt}Prefer not to say

{txt}{hline}
{res}w1_q9_2{right:9.2 How often: The invading Russian or pro-Russian forces have directly attacked}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels11_wave1}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}0{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       646{col 32}       1{col 42}{txt}Never
{col 20}{res}       100{col 32}       2{col 42}{txt}Once
{col 20}{res}        96{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        75{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}       105{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        59{col 32}       6{col 42}{txt}Prefer not to say

{txt}{hline}
{res}w1_q9_3{right:9.3 How often: The invading Russian or pro-Russian forces have directly attacked}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels11_wave1}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}0{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       417{col 32}       1{col 42}{txt}Never
{col 20}{res}       136{col 32}       2{col 42}{txt}Once
{col 20}{res}       169{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}       107{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}       197{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        55{col 32}       6{col 42}{txt}Prefer not to say

{com}. tab1 w1_q9_1 w1_q9_2 w1_q9_3

{res}-> tabulation of w1_q9_1  

{txt}9.1 How often: The {c |}
  invading Russian {c |}
    or pro-Russian {c |}
       forces have {c |}
 directly attacked {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        803       74.28       74.28
{txt}              Once {c |}{res}         81        7.49       81.78
{txt}      2 to 4 times {c |}{res}         57        5.27       87.05
{txt}     5 to 10 times {c |}{res}         28        2.59       89.64
{txt}More than 10 times {c |}{res}         44        4.07       93.71
{txt} Prefer not to say {c |}{res}         68        6.29      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,081      100.00

-> tabulation of w1_q9_2  

{txt}9.2 How often: The {c |}
  invading Russian {c |}
    or pro-Russian {c |}
       forces have {c |}
 directly attacked {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        646       59.76       59.76
{txt}              Once {c |}{res}        100        9.25       69.01
{txt}      2 to 4 times {c |}{res}         96        8.88       77.89
{txt}     5 to 10 times {c |}{res}         75        6.94       84.83
{txt}More than 10 times {c |}{res}        105        9.71       94.54
{txt} Prefer not to say {c |}{res}         59        5.46      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,081      100.00

-> tabulation of w1_q9_3  

{txt}9.3 How often: The {c |}
  invading Russian {c |}
    or pro-Russian {c |}
       forces have {c |}
 directly attacked {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        417       38.58       38.58
{txt}              Once {c |}{res}        136       12.58       51.16
{txt}      2 to 4 times {c |}{res}        169       15.63       66.79
{txt}     5 to 10 times {c |}{res}        107        9.90       76.69
{txt}More than 10 times {c |}{res}        197       18.22       94.91
{txt} Prefer not to say {c |}{res}         55        5.09      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,081      100.00
{txt}
{com}. recode w1_q9_1 w1_q9_2 w1_q9_3 (6=.)
{txt}(68 changes made to {bf:w1_q9_1})
(59 changes made to {bf:w1_q9_2})
(55 changes made to {bf:w1_q9_3})

{com}. rename w1_q9_1 w1_victim_self
{res}{txt}
{com}. rename w1_q9_2 w1_victim_family
{res}{txt}
{com}. rename w1_q9_3 w1_victim_other
{res}{txt}
{com}. corr w1_victim_self w1_victim_family w1_victim_other
{txt}(obs=993)

             {c |} w1_vic~f w1_vic~y w1_vic~r
{hline 13}{c +}{hline 27}
w1_victim_~f {c |}{res}   1.0000
{txt}w1_victim_~y {c |}{res}   0.4590   1.0000
{txt}w1_victim_~r {c |}{res}   0.3194   0.6462   1.0000

{txt}
{com}. alpha w1_victim_self w1_victim_family w1_victim_other

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8512443
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7277
{txt}
{com}. 
. ** Generates victimization scale
. egen Victimization_W1_5 = rowmean(w1_victim_self w1_victim_family w1_victim_other)
{txt}(38 missing values generated)

{com}. generate Victimization_1 = (Victimization_W1_5-1)/4
{txt}(38 missing values generated)

{com}. summ Victimization_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~1 {c |}{res}      1,043    .2472435    .2787995          0          1
{txt}
{com}. 
. 
. 
. ******************************************** Identification with Ukraine, Russia and Europe ******************************************************
. ** Ukraine
. recode w1_q23_1 (8=.), generate(Ukraine_ID_W1_7)
{txt}(29 differences between {bf:w1_q23_1} and {bf:Ukraine_ID_W1_7})

{com}. recode w1_q24_1 (8=.), generate(Ukraine_close_W1_7)
{txt}(24 differences between {bf:w1_q24_1} and {bf:Ukraine_close_W1_7})

{com}. corr Ukraine_ID_W1_7 Ukraine_close_W1_7
{txt}(obs=1,045)

             {c |} U~D_W1_7 U~e_W1_7
{hline 13}{c +}{hline 18}
Ukraine_ID~7 {c |}{res}   1.0000
{txt}Ukraine_cl~7 {c |}{res}   0.7665   1.0000

{txt}
{com}. egen ID_Ukraine_W1_7 = rowmean(Ukraine_ID_W1_7 Ukraine_close_W1_7)
{txt}(17 missing values generated)

{com}. generate ID_Ukraine_1 = (ID_Ukraine_W1_7-1)/6
{txt}(17 missing values generated)

{com}. 
. ** Russia
. recode w1_q23_2 (8=.), generate(Russia_ID_W1_7)
{txt}(48 differences between {bf:w1_q23_2} and {bf:Russia_ID_W1_7})

{com}. recode w1_q24_2 (8=.), generate(Russia_close_W1_7)
{txt}(37 differences between {bf:w1_q24_2} and {bf:Russia_close_W1_7})

{com}. corr Russia_ID_W1_7 Russia_close_W1_7
{txt}(obs=1,025)

             {c |} R~D_W1_7 R~e_W1_7
{hline 13}{c +}{hline 18}
Russia_ID_~7 {c |}{res}   1.0000
{txt}Russia_clo~7 {c |}{res}   0.7635   1.0000

{txt}
{com}. egen ID_Russia_W1_7 = rowmean(Russia_ID_W1_7 Russia_close_W1_7)
{txt}(29 missing values generated)

{com}. generate ID_Russia_1 = (ID_Russia_W1_7-1)/6
{txt}(29 missing values generated)

{com}. 
. ** Europe
. recode w1_q23_3 (8=.), generate(Europe_ID_W1_7)
{txt}(40 differences between {bf:w1_q23_3} and {bf:Europe_ID_W1_7})

{com}. recode w1_q24_3 (8=.), generate(Europe_close_W1_7)
{txt}(27 differences between {bf:w1_q24_3} and {bf:Europe_close_W1_7})

{com}. corr Europe_ID_W1_7 Europe_close_W1_7
{txt}(obs=1,037)

             {c |} E~D_W1_7 E~e_W1_7
{hline 13}{c +}{hline 18}
Europe_ID_~7 {c |}{res}   1.0000
{txt}Europe_clo~7 {c |}{res}   0.7978   1.0000

{txt}
{com}. egen ID_Europe_W1_7 = rowmean(Europe_ID_W1_7 Europe_close_W1_7)
{txt}(23 missing values generated)

{com}. generate ID_Europe_1 = (ID_Europe_W1_7-1)/6
{txt}(23 missing values generated)

{com}. 
. summ ID_Ukraine_1 ID_Russia_1 ID_Europe_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_1 {c |}{res}      1,064    .9348371    .1526613          0          1
{txt}{space 1}ID_Russia_1 {c |}{res}      1,052    .1351394    .2279704          0          1
{txt}{space 1}ID_Europe_1 {c |}{res}      1,058    .7063642    .2693415          0          1
{txt}
{com}. 
. 
. 
. 
. **************************************************************************************************************************************************
. *************************************************************** WAVE 2 ***************************************************************************
. **************************************************************************************************************************************************
. 
. ****************************************** Leadership trait preferences in IDEAL LEADER **********************************************************
. * Competent
. recode w2_q12_1 (8=.)
{txt}(28 changes made to {bf:w2_q12_1})

{com}. generate Competence_2 = (w2_q12_1-1)/6
{txt}(298 missing values generated)

{com}. 
. * Trustworthy
. recode w2_q12_2 (8=.)
{txt}(31 changes made to {bf:w2_q12_2})

{com}. generate Trustworthy_2 = (w2_q12_2-1)/6
{txt}(301 missing values generated)

{com}. 
. * Dominant
. recode w2_q12_3 (8=.)
{txt}(50 changes made to {bf:w2_q12_3})

{com}. generate Dominant_2 = (w2_q12_3-1)/6
{txt}(320 missing values generated)

{com}. 
. * Generous
. recode w2_q12_4 (8=.)
{txt}(38 changes made to {bf:w2_q12_4})

{com}. generate Generous_2 = (w2_q12_4-1)/6
{txt}(308 missing values generated)

{com}. 
. * Strong
. recode w2_q12_5 (8=.)
{txt}(27 changes made to {bf:w2_q12_5})

{com}. generate Strong_2 = (w2_q12_5-1)/6
{txt}(297 missing values generated)

{com}. 
. * Warm
. recode w2_q12_6 (8=.)
{txt}(45 changes made to {bf:w2_q12_6})

{com}. generate Warm_2 = (w2_q12_6-1)/6
{txt}(315 missing values generated)

{com}. 
. * Tough-minded
. recode w2_q12_7 (8=.)
{txt}(45 changes made to {bf:w2_q12_7})

{com}. generate Toughminded_2 = (w2_q12_7-1)/6
{txt}(315 missing values generated)

{com}. 
. summ Competence_2 Trustworthy_2 Dominant_2 Generous_2 Strong_2 Warm_2 Toughminded_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Competence_2 {c |}{res}        783    .8997446    .1773713          0          1
{txt}Trustworth~2 {c |}{res}        780    .9213675    .1557599          0          1
{txt}{space 2}Dominant_2 {c |}{res}        761    .5516864    .2980696          0          1
{txt}{space 2}Generous_2 {c |}{res}        773    .6836999    .2757289          0          1
{txt}{space 4}Strong_2 {c |}{res}        784    .8988095    .1647398          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}Warm_2 {c |}{res}        766    .6668842    .2672514          0          1
{txt}Toughminde~2 {c |}{res}        766    .5047868    .2929434          0          1
{txt}
{com}. 
. *** Exploring dimensions in trait impressions of IDEAL LEADER based on Principal Component Analysis
. factor Competence_2 Trustworthy_2 Dominant_2 Generous_2 Strong_2 Warm_2 Toughminded_2, pcf
{txt}(obs=741)

Factor analysis/correlation{col 50}Number of obs    = {res}       741
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.58584      1.11377            0.3694       0.3694
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.47207      0.29144            0.2103       0.5797
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.18063      0.62712            0.1687       0.7484
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.55350      0.07261            0.0791       0.8274
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.48090      0.10175            0.0687       0.8961
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.37914      0.03122            0.0542       0.9503
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.34793            .            0.0497       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1362.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6970}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0107}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3653}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3806}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7532}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0689}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4252}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2472}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4018}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6760}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3715}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2436}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6212}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3821}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5270}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1903}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7491}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1092}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3606}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2969}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6091}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4277}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5186}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1772}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2308}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8182}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2274}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2256}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate, oblique oblimin

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       741
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: oblique oblimin (Kaiser off){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |}     Variance   Proportion    Rotated factors are correlated
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.27768       0.3254
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.88209       0.2689
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.57754       0.2254
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1362.36{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7857}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0036}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0012}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3806}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8711}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0193}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0836}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2472}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0055}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1415}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8504}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2436}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0074}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8958}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0259}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1903}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8215}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0191}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1021}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2969}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0044}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.9064}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0210}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1772}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0037}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1228}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8766}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2256}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8836}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.6759}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.3542}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.0144}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.4518}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8700}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.4681}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5823}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.3429}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. 
. * Creates factor score variables for robustness tests of main results
. predict Comp_PCA_2 Warm_PCA_2 Domi_PCA_2
{txt}(option {bf:regression} assumed; regression scoring)

{p 0 0 2}Scoring coefficients (method = regression; based on oblimin(0) rotated factors){p_end}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.38310}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00123}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00427}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.42526}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.00830}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.06105}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00338}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.08076}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.56248}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00042}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.53956}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.01238}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.40001}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.01559}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.06241}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00167}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.54622}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.01872}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00330}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.07862}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.58121}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}


{com}. corr Comp_PCA_2 Warm_PCA_2 Domi_PCA_2
{txt}(obs=741)

             {c |} Comp_P~2 Warm_P~2 Domi_P~2
{hline 13}{c +}{hline 27}
  Comp_PCA_2 {c |}{res}   1.0000
  {txt}Warm_PCA_2 {c |}{res}   0.3181   1.0000
  {txt}Domi_PCA_2 {c |}{res}   0.1650   0.0460   1.0000

{txt}
{com}. 
. *** Main outcome variables: Composite scales for dominance, warmth and competence (on 0-1 scales)
. egen Domi_scale_2 = rowmean(Dominant_2 Toughminded_2)
{txt}(306 missing values generated)

{com}. 
. egen Comp_scale_2 = rowmean(Competence_2 Trustworthy_2 Strong_2)
{txt}(291 missing values generated)

{com}. 
. egen Warm_scale_2 = rowmean(Warm_2 Generous_2)
{txt}(305 missing values generated)

{com}. 
. summ Domi_scale_2 Comp_scale_2 Warm_scale_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale_2 {c |}{res}        775    .5288172    .2583886          0          1
{txt}Comp_scale_2 {c |}{res}        790     .906083    .1372004          0          1
{txt}Warm_scale_2 {c |}{res}        776    .6743986    .2480989          0          1
{txt}
{com}. corr Domi_scale_2 Comp_scale_2 Warm_scale_2
{txt}(obs=767)

             {c |} Domi_s~2 Comp_s~2 Warm_s~2
{hline 13}{c +}{hline 27}
Domi_scale_2 {c |}{res}   1.0000
{txt}Comp_scale_2 {c |}{res}   0.1922   1.0000
{txt}Warm_scale_2 {c |}{res}   0.0637   0.3291   1.0000

{txt}
{com}. 
. 
. ******************************************* Self-reported emotional reactions over last week *****************************************************
. * Afraid
. recode w2_q11_1 (8=.), generate(afraid_2)
{txt}(9 differences between {bf:w2_q11_1} and {bf:afraid_2})

{com}. * Frightened
. recode w2_q11_2 (8=.), generate(frightened_2)
{txt}(8 differences between {bf:w2_q11_2} and {bf:frightened_2})

{com}. * Scared
. recode w2_q11_3 (8=.), generate(scared_2)
{txt}(7 differences between {bf:w2_q11_3} and {bf:scared_2})

{com}. 
. ** Composite scale for anxiety
. corr afraid_2 frightened_2 scared_2
{txt}(obs=799)

             {c |} afraid_2 fright~2 scared_2
{hline 13}{c +}{hline 27}
    afraid_2 {c |}{res}   1.0000
{txt}frightened_2 {c |}{res}   0.7676   1.0000
    {txt}scared_2 {c |}{res}   0.7061   0.7152   1.0000

{txt}
{com}. alpha afraid_2 frightened_2 scared_2

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 2.187711
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8899
{txt}
{com}. egen fearfull_scale_W2_7 = rowmean(afraid_2 frightened_2 scared_2)
{txt}(275 missing values generated)

{com}. generate fearfull_scale_2 = (fearfull_scale_W2_7-1)/6
{txt}(275 missing values generated)

{com}. 
. 
. * Angry
. recode w2_q11_4 (8=.), generate(angry_2)
{txt}(9 differences between {bf:w2_q11_4} and {bf:angry_2})

{com}. * Hostile
. recode w2_q11_5 (8=.), generate(hostile_2)
{txt}(10 differences between {bf:w2_q11_5} and {bf:hostile_2})

{com}. * Disgusted
. recode w2_q11_6 (8=.), generate(disgusted_2)
{txt}(12 differences between {bf:w2_q11_6} and {bf:disgusted_2})

{com}. 
. ** Composite scale for agressive emotions
. corr angry_2 hostile_2 disgusted_2
{txt}(obs=795)

             {c |}  angry_2 hostil~2 disgus~2
{hline 13}{c +}{hline 27}
     angry_2 {c |}{res}   1.0000
   {txt}hostile_2 {c |}{res}   0.6010   1.0000
 {txt}disgusted_2 {c |}{res}   0.5372   0.5186   1.0000

{txt}
{com}. alpha angry_2 hostile_2 disgusted_2

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.436327
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7834
{txt}
{com}. egen aggressive_scale_W2_7 = rowmean(angry_2 hostile_2 disgusted_2)
{txt}(277 missing values generated)

{com}. generate aggressive_scale_2 = (aggressive_scale_W2_7-1)/6
{txt}(277 missing values generated)

{com}. 
. 
. * Sad
. recode w2_q11_7 (8=.), generate(sad_2)
{txt}(7 differences between {bf:w2_q11_7} and {bf:sad_2})

{com}. * Lonely
. recode w2_q11_8 (8=.), generate(lonely_2)
{txt}(10 differences between {bf:w2_q11_8} and {bf:lonely_2})

{com}. * Downhearted
. recode w2_q11_9 (8=.), generate(downhearted_2)
{txt}(11 differences between {bf:w2_q11_9} and {bf:downhearted_2})

{com}. 
. ** Composite scale for sadness
. corr sad_2 lonely_2 downhearted_2 
{txt}(obs=797)

             {c |}    sad_2 lonely_2 downhe~2
{hline 13}{c +}{hline 27}
       sad_2 {c |}{res}   1.0000
    {txt}lonely_2 {c |}{res}   0.3580   1.0000
{txt}downhearte~2 {c |}{res}   0.6237   0.4347   1.0000

{txt}
{com}. alpha sad_2 lonely_2 downhearted_2 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.350082
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7259
{txt}
{com}. egen sadness_scale_W2_7 = rowmean(sad_2 lonely_2 downhearted_2)
{txt}(276 missing values generated)

{com}. generate sadness_scale_2 = (sadness_scale_W2_7-1)/6
{txt}(276 missing values generated)

{com}. 
. 
. * Proud
. recode w2_q11_10 (8=.), generate(proud_2)
{txt}(13 differences between {bf:w2_q11_10} and {bf:proud_2})

{com}. * Strong
. recode w2_q11_11 (8=.), generate(strong_2)
{txt}(11 differences between {bf:w2_q11_11} and {bf:strong_2})

{com}. * Confident
. recode w2_q11_12 (8=.), generate(confident_2)
{txt}(8 differences between {bf:w2_q11_12} and {bf:confident_2})

{com}. 
. ** Composite scale for self-confident emotions
. corr proud_2 strong_2 confident_2
{txt}(obs=793)

             {c |}  proud_2 strong_2 confid~2
{hline 13}{c +}{hline 27}
     proud_2 {c |}{res}   1.0000
    {txt}strong_2 {c |}{res}   0.5883   1.0000
 {txt}confident_2 {c |}{res}   0.5508   0.7228   1.0000

{txt}
{com}. alpha proud_2 strong_2 confident_2

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.534326
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8307
{txt}
{com}. egen selfconf_scale_W2_7 = rowmean(proud_2 strong_2 confident_2)
{txt}(276 missing values generated)

{com}. generate selfconf_scale_2 = (selfconf_scale_W2_7-1)/6
{txt}(276 missing values generated)

{com}. 
. summ fearfull_scale_2 aggressive_scale_2 sadness_scale_2 selfconf_scale_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~2 {c |}{res}        806    .4276261    .2618236          0          1
{txt}aggressive~2 {c |}{res}        804    .6732311    .2259084          0          1
{txt}sadness_sc~2 {c |}{res}        805    .5142857    .2271874          0          1
{txt}selfconf_s~2 {c |}{res}        805    .6017253    .2261692          0          1
{txt}
{com}. 
. 
. 
. ******************************************* Self-reported Victimization of Russian Attacks *******************************************************
. **** Recodes all three items
. codebook w2_q8_1 w2_q8_2 w2_q8_3

{txt}{hline}
{res}w2_q8_1{right:8. Please tell us how often the events described below have happened over the la}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels12_wave2}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}270{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       653{col 32}       1{col 42}{txt}Never
{col 20}{res}        43{col 32}       2{col 42}{txt}Once
{col 20}{res}        37{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        23{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}        16{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        39{col 32}       6{col 42}{txt}Prefer not to say
{col 20}{res}       270{col 32}       .{col 42}

{txt}{hline}
{res}w2_q8_2{right:8. Please tell us how often the events described below have happened over the la}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels12_wave2}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}270{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       498{col 32}       1{col 42}{txt}Never
{col 20}{res}        94{col 32}       2{col 42}{txt}Once
{col 20}{res}        92{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        36{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}        53{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        38{col 32}       6{col 42}{txt}Prefer not to say
{col 20}{res}       270{col 32}       .{col 42}

{txt}{hline}
{res}w2_q8_3{right:8. Please tell us how often the events described below have happened over the la}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels12_wave2}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}270{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       307{col 32}       1{col 42}{txt}Never
{col 20}{res}       114{col 32}       2{col 42}{txt}Once
{col 20}{res}       146{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        78{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}       123{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        43{col 32}       6{col 42}{txt}Prefer not to say
{col 20}{res}       270{col 32}       .{col 42}
{txt}
{com}. tab1 w2_q8_1 w2_q8_2 w2_q8_3

{res}-> tabulation of w2_q8_1  

 {txt}8. Please tell us {c |}
     how often the {c |}
  events described {c |}
        below have {c |}
 happened over the {c |}
                la {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        653       80.52       80.52
{txt}              Once {c |}{res}         43        5.30       85.82
{txt}      2 to 4 times {c |}{res}         37        4.56       90.38
{txt}     5 to 10 times {c |}{res}         23        2.84       93.22
{txt}More than 10 times {c |}{res}         16        1.97       95.19
{txt} Prefer not to say {c |}{res}         39        4.81      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        811      100.00

-> tabulation of w2_q8_2  

 {txt}8. Please tell us {c |}
     how often the {c |}
  events described {c |}
        below have {c |}
 happened over the {c |}
                la {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        498       61.41       61.41
{txt}              Once {c |}{res}         94       11.59       73.00
{txt}      2 to 4 times {c |}{res}         92       11.34       84.34
{txt}     5 to 10 times {c |}{res}         36        4.44       88.78
{txt}More than 10 times {c |}{res}         53        6.54       95.31
{txt} Prefer not to say {c |}{res}         38        4.69      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        811      100.00

-> tabulation of w2_q8_3  

 {txt}8. Please tell us {c |}
     how often the {c |}
  events described {c |}
        below have {c |}
 happened over the {c |}
                la {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        307       37.85       37.85
{txt}              Once {c |}{res}        114       14.06       51.91
{txt}      2 to 4 times {c |}{res}        146       18.00       69.91
{txt}     5 to 10 times {c |}{res}         78        9.62       79.53
{txt}More than 10 times {c |}{res}        123       15.17       94.70
{txt} Prefer not to say {c |}{res}         43        5.30      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        811      100.00
{txt}
{com}. recode w2_q8_1 w2_q8_2 w2_q8_3 (6=.)
{txt}(39 changes made to {bf:w2_q8_1})
(38 changes made to {bf:w2_q8_2})
(43 changes made to {bf:w2_q8_3})

{com}. rename w2_q8_1 w2_victim_self
{res}{txt}
{com}. rename w2_q8_2 w2_victim_family
{res}{txt}
{com}. rename w2_q8_3 w2_victim_other
{res}{txt}
{com}. corr w2_victim_self w2_victim_family w2_victim_other
{txt}(obs=752)

             {c |} w2_vic~f w2_vic~y w2_vic~r
{hline 13}{c +}{hline 27}
w2_victim_~f {c |}{res}   1.0000
{txt}w2_victim_~y {c |}{res}   0.4004   1.0000
{txt}w2_victim_~r {c |}{res}   0.2456   0.6305   1.0000

{txt}
{com}. alpha w2_victim_self w2_victim_family w2_victim_other

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .6259643
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6847
{txt}
{com}. 
. 
. ** Generates victimization scale
. egen Victimization_W2_5 = rowmean(w2_victim_self w2_victim_family w2_victim_other)
{txt}(296 missing values generated)

{com}. generate Victimization_2 = (Victimization_W2_5-1)/4
{txt}(296 missing values generated)

{com}. summ Victimization_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~2 {c |}{res}        785    .2165074    .2450247          0          1
{txt}
{com}. 
. 
. ********************************************* Identification with Ukraine, Russia and Europe *****************************************************
. ** Ukraine
. recode w2_q13_1 (8=.), generate(Ukraine_ID_W2_7)
{txt}(12 differences between {bf:w2_q13_1} and {bf:Ukraine_ID_W2_7})

{com}. recode w2_q14_1 (8=.), generate(Ukraine_close_W2_7)
{txt}(10 differences between {bf:w2_q14_1} and {bf:Ukraine_close_W2_7})

{com}. corr Ukraine_ID_W2_7 Ukraine_close_W2_7
{txt}(obs=797)

             {c |} U~D_W2_7 U~e_W2_7
{hline 13}{c +}{hline 18}
Ukrai~D_W2_7 {c |}{res}   1.0000
{txt}Ukrai~e_W2_7 {c |}{res}   0.7919   1.0000

{txt}
{com}. egen ID_Ukraine_W2_7 = rowmean(Ukraine_ID_W2_7 Ukraine_close_W2_7)
{txt}(278 missing values generated)

{com}. generate ID_Ukraine_2 = (ID_Ukraine_W2_7-1)/6
{txt}(278 missing values generated)

{com}. 
. ** Russia
. recode w2_q13_2 (8=.), generate(Russia_ID_W2_7)
{txt}(15 differences between {bf:w2_q13_2} and {bf:Russia_ID_W2_7})

{com}. recode w2_q14_2 (8=.), generate(Russia_close_W2_7)
{txt}(14 differences between {bf:w2_q14_2} and {bf:Russia_close_W2_7})

{com}. corr Russia_ID_W2_7 Russia_close_W2_7
{txt}(obs=793)

             {c |} R~D_W2_7 R~e_W2_7
{hline 13}{c +}{hline 18}
Russia_I~2_7 {c |}{res}   1.0000
{txt}Russia_c~2_7 {c |}{res}   0.8163   1.0000

{txt}
{com}. egen ID_Russia_W2_7 = rowmean(Russia_ID_W2_7 Russia_close_W2_7)
{txt}(281 missing values generated)

{com}. generate ID_Russia_2 = (ID_Russia_W2_7-1)/6
{txt}(281 missing values generated)

{com}. 
. ** Europe
. recode w2_q13_3 (8=.), generate(Europe_ID_W2_7)
{txt}(21 differences between {bf:w2_q13_3} and {bf:Europe_ID_W2_7})

{com}. recode w2_q14_3 (8=.), generate(Europe_close_W2_7)
{txt}(19 differences between {bf:w2_q14_3} and {bf:Europe_close_W2_7})

{com}. corr Europe_ID_W2_7 Europe_close_W2_7
{txt}(obs=786)

             {c |} E~D_W2_7 E~e_W2_7
{hline 13}{c +}{hline 18}
Europe_I~2_7 {c |}{res}   1.0000
{txt}Europe_c~2_7 {c |}{res}   0.8779   1.0000

{txt}
{com}. egen ID_Europe_W2_7 = rowmean(Europe_ID_W2_7 Europe_close_W2_7)
{txt}(285 missing values generated)

{com}. generate ID_Europe_2 = (ID_Europe_W2_7-1)/6
{txt}(285 missing values generated)

{com}. 
. summ ID_Ukraine_2 ID_Russia_2 ID_Europe_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_2 {c |}{res}        803    .9432337    .1384083          0          1
{txt}{space 1}ID_Russia_2 {c |}{res}        800    .0653125    .1658194          0          1
{txt}{space 1}ID_Europe_2 {c |}{res}        796    .6871859    .2807245          0          1
{txt}
{com}. 
. 
. 
. ********************************** Creates variable for whether data for all trait rating variables is present ***********************************
. summ Domi_scale_2 Comp_scale_2 Warm_scale_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale_2 {c |}{res}        775    .5288172    .2583886          0          1
{txt}Comp_scale_2 {c |}{res}        790     .906083    .1372004          0          1
{txt}Warm_scale_2 {c |}{res}        776    .6743986    .2480989          0          1
{txt}
{com}. generate include = .
{txt}(1,081 missing values generated)

{com}. replace include = 1 if Domi_scale_1 !=. & Warm_scale_1 !=. & Comp_scale_1 !=. & Domi_scale_2 !=. & Warm_scale_2 !=. & Comp_scale_2 !=.
{txt}(753 real changes made)

{com}. tab include

    {txt}include {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        753      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        753      100.00
{txt}
{com}. 
. ** Creates similar variable based on PCA
. generate include_PCA = .
{txt}(1,081 missing values generated)

{com}. replace include_PCA = 1 if Domi_PCA_1 !=. & Warm_PCA_1 !=. & Comp_PCA_1 !=. & Domi_PCA_2 !=. & Warm_PCA_2 !=. & Comp_PCA_2 !=.
{txt}(704 real changes made)

{com}. tab include_PCA

{txt}include_PCA {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        704      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        704      100.00
{txt}
{com}. 
. 
. ****************************************** Experimental treatment for Ideal Leader Experiment in Wave 2 ******************************************
. * Experimental treatment for leader trait evaluation questions; codes all respondents to be assigned to think of "Conflict, now"
. generate Conflict_2=1
{txt}
{com}. 
. 
. 
. **************************************************************************************************************************************************
. ******************************************************* Difference scores ************************************************************************
. **************************************************************************************************************************************************
. 
. *** Leader trait preferences in ideal leaders
. * Composite scales
. generate Domi_scale_diff = Domi_scale_2 - Domi_scale_1
{txt}(317 missing values generated)

{com}. generate Comp_scale_diff = Comp_scale_2 - Comp_scale_1
{txt}(302 missing values generated)

{com}. generate Warm_scale_diff = Warm_scale_2 - Warm_scale_1
{txt}(316 missing values generated)

{com}. 
. summ Domi_scale_diff Comp_scale_diff Warm_scale_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale~f {c |}{res}        764    .0224695    .2304394         -1   .8333333
{txt}Comp_scale~f {c |}{res}        779    .0018186    .1368881  -.8333333   .8333333
{txt}Warm_scale~f {c |}{res}        765    -.046732    .2221718  -.9166666   .8333333
{txt}
{com}. 
. * Single-item traits
. generate Dominance_diff = Dominant_2 - Dominant_1
{txt}(339 missing values generated)

{com}. generate Toughminded_diff = Toughminded_2 - Toughminded_1
{txt}(331 missing values generated)

{com}. 
. generate Competence_diff = Competence_2 - Competence_1
{txt}(328 missing values generated)

{com}. generate Trustworthy_diff = Trustworthy_2 - Trustworthy_1
{txt}(317 missing values generated)

{com}. generate Strong_diff = Strong_2 - Strong_1
{txt}(308 missing values generated)

{com}. 
. generate Warm_diff = Warm_2 - Warm_1
{txt}(331 missing values generated)

{com}. generate Generous_diff = Generous_2 - Generous_1
{txt}(324 missing values generated)

{com}. 
. summ Dominance_diff Toughminded_diff Competence_diff Trustworthy_diff Strong_diff Warm_diff Generous_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Dominance_~f {c |}{res}        742   -.0404313    .2808646         -1          1
{txt}Toughminde~f {c |}{res}        750         .08    .2888164         -1          1
{txt}Competence~f {c |}{res}        753    .0055334     .197705         -1          1
{txt}Trustworth~f {c |}{res}        764   -.0119983    .1558341         -1   .8333333
{txt}{space 1}Strong_diff {c |}{res}        773    .0097025    .1809887         -1   .8333333
{txt}{hline 13}{c +}{hline 57}
{space 3}Warm_diff {c |}{res}        750   -.0431111    .2538692         -1          1
{txt}Generous_d~f {c |}{res}        757   -.0464553    .2611553         -1   .8333333
{txt}
{com}. 
. 
. * Variables based on PCA results
. generate Comp_PCA_diff = Comp_PCA_2 - Comp_PCA_1
{txt}(377 missing values generated)

{com}. generate Warm_PCA_diff = Warm_PCA_2 - Warm_PCA_1
{txt}(377 missing values generated)

{com}. generate Domi_PCA_diff = Domi_PCA_2 - Domi_PCA_1
{txt}(377 missing values generated)

{com}. 
. 
. *** Emotional reactions
. generate fearfull_diff = fearfull_scale_2 - fearfull_scale_1
{txt}(281 missing values generated)

{com}. generate aggressive_diff = aggressive_scale_2 - aggressive_scale_1
{txt}(281 missing values generated)

{com}. 
. generate sadness_diff = sadness_scale_2 - sadness_scale_1
{txt}(280 missing values generated)

{com}. generate selfconf_diff = selfconf_scale_2 - selfconf_scale_1
{txt}(280 missing values generated)

{com}. 
. summ fearfull_diff aggressive_diff sadness_diff selfconf_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_d~f {c |}{res}        800   -.0549306    .2021551  -.8333333   .5555556
{txt}aggressive~f {c |}{res}        800    .0198611    .1971963  -.8333333   .7777777
{txt}sadness_diff {c |}{res}        801   -.0074906    .1955764  -.6666666   .7777778
{txt}selfconf_d~f {c |}{res}        801   -.0044736    .1874851  -.6944444          1
{txt}
{com}. 
. *** Identification with Ukraine, Russia and Europe
. generate ID_Ukraine_diff = ID_Ukraine_2  - ID_Ukraine_1
{txt}(284 missing values generated)

{com}. generate ID_Europe_diff = ID_Europe_2 - ID_Europe_1
{txt}(292 missing values generated)

{com}. generate ID_Russia_diff = ID_Russia_2 - ID_Russia_1
{txt}(295 missing values generated)

{com}. 
. summ ID_Ukraine_diff ID_Europe_diff ID_Russia_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine~f {c |}{res}        797   -.0003137     .110713  -.8333333          1
{txt}ID_Europe_~f {c |}{res}        789   -.0362273    .2276356         -1          1
{txt}ID_Russia_~f {c |}{res}        786   -.0483461    .1585455       -.75   .6666667
{txt}
{com}. 
. *** Victimization of Russian attacks
. generate Victimization_diff = Victimization_2- Victimization_1
{txt}(310 missing values generated)

{com}. 
. summ Victimization_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~f {c |}{res}        771   -.0368029    .2661178         -1   .9166666
{txt}
{com}. 
. 
. 
. 
. 
. 
. **************************************************************************************************************************************************
. ************************************* SOM.1: Descriptive statistics for key variables across waves ***********************************************
. **************************************************************************************************************************************************
. 
. **** Demographics across waves
. * Wave 1
. tab sex

  {txt}RECODE of {c |}
  w1_q3 (3. {c |}
       Sex) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        470       43.48       43.48
{txt}     Female {c |}{res}        611       56.52      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. summ age

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      1,081    35.61332    8.730129         18         55
{txt}
{com}. tab education

    {txt}RECODE of w1_q6 (6. What is the {c |}
highest level of education that you {c |}
                       have complet {c |}      Freq.     Percent        Cum.
{hline 36}{c +}{hline 35}
             Primary or High school {c |}{res}         95        8.86        8.86
{txt}Professional-technical (vocational) {c |}{res}        185       17.26       26.12
{txt}                  Incomplete higher {c |}{res}         85        7.93       34.05
{txt}                    Bachelor degree {c |}{res}        188       17.54       51.59
{txt}          Master degree & Doctorate {c |}{res}        519       48.41      100.00
{txt}{hline 36}{c +}{hline 35}
                              Total {c |}{res}      1,072      100.00
{txt}
{com}. tab region

     {txt}Region {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       East {c |}{res}        137       12.67       12.67
{txt}       West {c |}{res}        171       15.82       28.49
{txt}       Kyiv {c |}{res}        195       18.04       46.53
{txt}      North {c |}{res}        112       10.36       56.89
{txt}     Centre {c |}{res}        273       25.25       82.15
{txt}      South {c |}{res}        193       17.85      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. 
. * wave 2
. tab sex if include == 1

  {txt}RECODE of {c |}
  w1_q3 (3. {c |}
       Sex) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        308       40.90       40.90
{txt}     Female {c |}{res}        445       59.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        753      100.00
{txt}
{com}. summ age if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        753    36.32537    8.547372         18         54
{txt}
{com}. tab education if include == 1

    {txt}RECODE of w1_q6 (6. What is the {c |}
highest level of education that you {c |}
                       have complet {c |}      Freq.     Percent        Cum.
{hline 36}{c +}{hline 35}
             Primary or High school {c |}{res}         57        7.60        7.60
{txt}Professional-technical (vocational) {c |}{res}        121       16.13       23.73
{txt}                  Incomplete higher {c |}{res}         57        7.60       31.33
{txt}                    Bachelor degree {c |}{res}        134       17.87       49.20
{txt}          Master degree & Doctorate {c |}{res}        381       50.80      100.00
{txt}{hline 36}{c +}{hline 35}
                              Total {c |}{res}        750      100.00
{txt}
{com}. tab region if include == 1

     {txt}Region {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       East {c |}{res}         83       11.02       11.02
{txt}       West {c |}{res}        123       16.33       27.36
{txt}       Kyiv {c |}{res}        143       18.99       46.35
{txt}      North {c |}{res}         78       10.36       56.71
{txt}     Centre {c |}{res}        194       25.76       82.47
{txt}      South {c |}{res}        132       17.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        753      100.00
{txt}
{com}. 
. 
. **** Trait rating scales
. * Wave 1
. summ Comp_scale_1 Warm_scale_1 Domi_scale_1 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scale_1 {c |}{res}      1,057    .8936718     .145519          0          1
{txt}Warm_scale_1 {c |}{res}      1,055    .7185624    .2263167          0          1
{txt}Domi_scale_1 {c |}{res}      1,051     .512052    .2588131          0          1
{txt}
{com}. alpha Competence_1 Trustworthy_1 Strong_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0169582
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8117
{txt}
{com}. alpha Warm_1 Generous_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0414576
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8063
{txt}
{com}. alpha Dominant_1 Toughminded_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .042429
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.6459
{txt}
{com}. 
. 
. * Wave 2
. summ Comp_scale_2 Warm_scale_2 Domi_scale_2 if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scale_2 {c |}{res}        753     .908219     .132078          0          1
{txt}Warm_scale_2 {c |}{res}        753    .6737494    .2455379          0          1
{txt}Domi_scale_2 {c |}{res}        753    .5281098    .2580758          0          1
{txt}
{com}. alpha Competence_2 Trustworthy_2 Strong_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .012883
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7408
{txt}
{com}. alpha Warm_2 Generous_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}   .04601
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.7738
{txt}
{com}. alpha Dominant_2 Toughminded_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0438885
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.6701
{txt}
{com}. 
. **** Emotional reactions
. * Wave 1
. summ fearfull_scale_1 aggressive_scale_1 sadness_scale_1 selfconf_scale_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~1 {c |}{res}      1,070    .4757529    .2744772          0          1
{txt}aggressive~1 {c |}{res}      1,070    .6362669    .2370329          0          1
{txt}sadness_sc~1 {c |}{res}      1,074    .5127767    .2336689          0          1
{txt}selfconf_s~1 {c |}{res}      1,070    .6069574    .2299484          0          1
{txt}
{com}. alpha afraid_1 frightened_1 scared_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  2.41545
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8953
{txt}
{com}. alpha sad_1 lonely_1 downhearted_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.404803
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7254
{txt}
{com}. alpha proud_1 strong_1 confident_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.457104
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7866
{txt}
{com}. alpha angry_1 hostile_1 disgusted_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.456529
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7630
{txt}
{com}. 
. * wave 2
. summ fearfull_scale_2 aggressive_scale_2 sadness_scale_2 selfconf_scale_2 if include==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~2 {c |}{res}        752    .4295582    .2609825          0          1
{txt}aggressive~2 {c |}{res}        750    .6693333    .2272125          0          1
{txt}sadness_sc~2 {c |}{res}        751    .5145362    .2254564          0          1
{txt}selfconf_s~2 {c |}{res}        751    .6013094     .226263          0          1
{txt}
{com}. alpha afraid_2 frightened_2 scared_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  2.18168
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8898
{txt}
{com}. alpha sad_2 lonely_2 downhearted_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.319197
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7195
{txt}
{com}. alpha proud_2 strong_2 confident_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.528404
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8291
{txt}
{com}. alpha angry_2 hostile_2 disgusted_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.449787
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7818
{txt}
{com}. 
. 
. **** Identities
. * Wave 1
. sum ID_Ukraine_1 ID_Europe_1 ID_Russia_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_1 {c |}{res}      1,064    .9348371    .1526613          0          1
{txt}{space 1}ID_Europe_1 {c |}{res}      1,058    .7063642    .2693415          0          1
{txt}{space 1}ID_Russia_1 {c |}{res}      1,052    .1351394    .2279704          0          1
{txt}
{com}. alpha Ukraine_ID_W1_7 Ukraine_close_W1_7

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .7195476
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8640
{txt}
{com}. alpha Russia_ID_W1_7 Russia_close_W1_7

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.607426
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8649
{txt}
{com}. alpha Europe_ID_W1_7 Europe_close_W1_7

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 2.259844
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8826
{txt}
{com}. 
. * Wave 2
. sum ID_Ukraine_2 ID_Europe_2 ID_Russia_2 if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_2 {c |}{res}        748     .943516    .1398345          0          1
{txt}{space 1}ID_Europe_2 {c |}{res}        745    .6887025    .2810555          0          1
{txt}{space 1}ID_Russia_2 {c |}{res}        746    .0654602    .1666578          0          1
{txt}
{com}. alpha Ukraine_ID_W2_7 Ukraine_close_W2_7 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .6294436
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8920
{txt}
{com}. alpha Russia_ID_W2_7 Russia_close_W2_7 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8761365
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.9005
{txt}
{com}. alpha Europe_ID_W2_7 Europe_close_W2_7 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 2.632705
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.9325
{txt}
{com}. 
. ** Victimization
. summ Victimization_1 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~1 {c |}{res}      1,043    .2472435    .2787995          0          1
{txt}
{com}. alpha w1_victim_self w1_victim_family w1_victim_other

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8512443
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7277
{txt}
{com}. summ Victimization_2 if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~2 {c |}{res}        738    .2140357    .2394265          0          1
{txt}
{com}. alpha w2_victim_self w2_victim_family w2_victim_other if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .5939174
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6716
{txt}
{com}. 
. 
. **************************************************************************************************************************************************
. **************************************************************** MAIN ANALYSES *******************************************************************
. **************************************************************************************************************************************************
. 
. ********************************************** MAPPING WARTIME LEADER TRAIT PREFERENCES **********************************************************
. *** Key results reported in main text and full models in SOM.2
. reg Comp_scale_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 7.13159372       373  .019119554   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 7.13159372       373  .019119554   {txt}Root MSE        =   {res} .13827

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8989899{col 26}{space 2}   .00715{col 37}{space 1}  125.73{col 46}{space 3}0.000{col 54}{space 4} .8849306{col 67}{space 3} .9130492
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Comp_scale_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 6.52602185       373  .017496037   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 6.52602185       373  .017496037   {txt}Root MSE        =   {res} .13227

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9091652{col 26}{space 2} .0068397{col 37}{space 1}  132.93{col 46}{space 3}0.000{col 54}{space 4} .8957161{col 67}{space 3} .9226143
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_scale_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 19.7236508       373   .05287842   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 19.7236508       373   .05287842   {txt}Root MSE        =   {res} .22995

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7034314{col 26}{space 2} .0118906{col 37}{space 1}   59.16{col 46}{space 3}0.000{col 54}{space 4} .6800504{col 67}{space 3} .7268124
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 24.7685853       373  .066403714   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 24.7685853       373  .066403714   {txt}Root MSE        =   {res} .25769

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6691176{col 26}{space 2} .0133248{col 37}{space 1}   50.22{col 46}{space 3}0.000{col 54}{space 4} .6429165{col 67}{space 3} .6953188
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Domi_scale_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 25.3232503       373  .067890751   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 25.3232503       373  .067890751   {txt}Root MSE        =   {res} .26056

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5274064{col 26}{space 2} .0134732{col 37}{space 1}   39.14{col 46}{space 3}0.000{col 54}{space 4} .5009135{col 67}{space 3} .5538993
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 27.3532561       373  .073333126   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 27.3532561       373  .073333126   {txt}Root MSE        =   {res}  .2708

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5211676{col 26}{space 2} .0140028{col 37}{space 1}   37.22{col 46}{space 3}0.000{col 54}{space 4} .4936333{col 67}{space 3} .5487019
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *** Tests differences between traits in wave 1
. ttest Comp_scale_1==Warm_scale_1 if Conflict_1 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    374{col 22} .8989899{col 34}   .00715{col 46} .1382735{col 58} .8849306{col 70} .9130492
{txt}Warm~e_1 {c |}{res}{col 12}    374{col 22} .7034314{col 34} .0118906{col 46} .2299531{col 58} .6800504{col 70} .7268124
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    374{col 22} .1955585{col 34} .0117395{col 46} .2270313{col 58} .1724746{col 70} .2186424
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Warm_scale_1{txt})               t = {res} 16.6582
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     373

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Comp_scale_1==Domi_scale_1 if Conflict_1 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    374{col 22} .8989899{col 34}   .00715{col 46} .1382735{col 58} .8849306{col 70} .9130492
{txt}Domi~e_1 {c |}{res}{col 12}    374{col 22} .5274064{col 34} .0134732{col 46} .2605585{col 58} .5009135{col 70} .5538993
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    374{col 22} .3715835{col 34} .0133583{col 46} .2583376{col 58} .3453164{col 70} .3978505
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Domi_scale_1{txt})               t = {res} 27.8166
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     373

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Warm_scale_1==Domi_scale_1 if Conflict_1 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Warm~e_1 {c |}{res}{col 12}    374{col 22} .7034314{col 34} .0118906{col 46} .2299531{col 58} .6800504{col 70} .7268124
{txt}Domi~e_1 {c |}{res}{col 12}    374{col 22} .5274064{col 34} .0134732{col 46} .2605585{col 58} .5009135{col 70} .5538993
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    374{col 22}  .176025{col 34} .0165154{col 46} .3193936{col 58} .1435499{col 70}    .2085
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Warm_scale_1{txt} - {res}Domi_scale_1{txt})               t = {res} 10.6582
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     373

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *** Tests differences between traits in wave 2
. ttest Comp_scale_1==Warm_scale_2 if Conflict_2 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    753{col 22}  .907887{col 34} .0046071{col 46} .1264234{col 58} .8988426{col 70} .9169313
{txt}Warm_s~2 {c |}{res}{col 12}    753{col 22} .6737494{col 34} .0089479{col 46} .2455379{col 58} .6561836{col 70} .6913153
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    753{col 22} .2341375{col 34} .0094643{col 46} .2597079{col 58}  .215558{col 70} .2527171
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Warm_scale_2{txt})               t = {res} 24.7391
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     752

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Comp_scale_1==Domi_scale_2 if Conflict_2 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    753{col 22}  .907887{col 34} .0046071{col 46} .1264234{col 58} .8988426{col 70} .9169313
{txt}Domi_s~2 {c |}{res}{col 12}    753{col 22} .5281098{col 34} .0094048{col 46} .2580758{col 58}  .509647{col 70} .5465726
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    753{col 22} .3797772{col 34} .0097278{col 46} .2669391{col 58} .3606803{col 70}  .398874
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Domi_scale_2{txt})               t = {res} 39.0404
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     752

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Warm_scale_1==Domi_scale_2 if Conflict_2 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Warm~e_1 {c |}{res}{col 12}    753{col 22} .7201195{col 34}  .008101{col 46} .2222977{col 58} .7042163{col 70} .7360227
{txt}Domi_s~2 {c |}{res}{col 12}    753{col 22} .5281098{col 34} .0094048{col 46} .2580758{col 58}  .509647{col 70} .5465726
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    753{col 22} .1920097{col 34} .0118247{col 46} .3244801{col 58} .1687964{col 70} .2152231
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Warm_scale_1{txt} - {res}Domi_scale_2{txt})               t = {res} 16.2380
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     752

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *** Correlations between preferences for same traits across waves
. pwcorr Comp_scale_1 Comp_scale_2 if Conflict_1 == 1, sig

             {txt}{c |} Com~le_1 Comp_s~2
{hline 13}{c +}{hline 18}
Comp_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Comp_scale_2 {c |} {res}  0.4252   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Competence_1 Trustworthy_1 Strong_1 Competence_2 Trustworthy_2 Strong_2 if Conflict_1 == 1, sig

             {txt}{c |} Compet~1 Trustw~1 Strong_1 Compet~2 Trustw~2 Strong_2
{hline 13}{c +}{hline 54}
Competence_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Trustworth~1 {c |} {res}  0.6473   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
    Strong_1 {c |} {res}  0.5173   0.5964   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
Competence_2 {c |} {res}  0.3561   0.2109   0.2188   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
Trustworth~2 {c |} {res}  0.2927   0.3245   0.2561   0.4250   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}
    Strong_2 {c |} {res}  0.2627   0.2959   0.3425   0.4040   0.4687   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. reg Comp_scale_1 Comp_scale_2 if Conflict_1 == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       389
{txt}{hline 13}{c +}{hline 34}   F(1, 387)       = {res}    85.40
{txt}       Model {c |} {res} 1.32159262         1  1.32159262   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5.98867591       387  .015474615   {txt}R-squared       ={res}    0.1808
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1787
{txt}       Total {c |} {res} 7.31026853       388  .018840898   {txt}Root MSE        =   {res}  .1244

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Comp_scale_2 {c |}{col 14}{res}{space 2} .4366544{col 26}{space 2} .0472497{col 37}{space 1}    9.24{col 46}{space 3}0.000{col 54}{space 4} .3437562{col 67}{space 3} .5295526
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5007107{col 26}{space 2} .0434152{col 37}{space 1}   11.53{col 46}{space 3}0.000{col 54}{space 4} .4153517{col 67}{space 3} .5860698
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. pwcorr Warm_scale_1 Warm_scale_2 if Conflict_1 == 1, sig

             {txt}{c |} Warm~e_1 Warm_s~2
{hline 13}{c +}{hline 18}
Warm_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Warm_scale_2 {c |} {res}  0.5508   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Generous_1 Warm_1  Generous_2 Warm_2 if Conflict_1 == 1, sig

             {txt}{c |} Genero~1   Warm_1 Genero~2   Warm_2
{hline 13}{c +}{hline 36}
  Generous_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
      Warm_1 {c |} {res}  0.6699   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
  Generous_2 {c |} {res}  0.4936   0.4403   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
      Warm_2 {c |} {res}  0.4482   0.4801   0.6290   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. reg Warm_scale_1 Warm_scale_2 if Conflict_1 == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       382
{txt}{hline 13}{c +}{hline 34}   F(1, 380)       = {res}   165.50
{txt}       Model {c |} {res}  6.0520385         1   6.0520385   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 13.8962587       380  .036569102   {txt}R-squared       ={res}    0.3034
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3016
{txt}       Total {c |} {res} 19.9482972       381  .052357735   {txt}Root MSE        =   {res} .19123

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Warm_scale_2 {c |}{col 14}{res}{space 2} .4847426{col 26}{space 2} .0376806{col 37}{space 1}   12.86{col 46}{space 3}0.000{col 54}{space 4} .4106541{col 67}{space 3} .5588311
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3802865{col 26}{space 2} .0270122{col 37}{space 1}   14.08{col 46}{space 3}0.000{col 54}{space 4} .3271744{col 67}{space 3} .4333986
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. pwcorr Domi_scale_1 Domi_scale_2 if Conflict_1 == 1, sig

             {txt}{c |} Domi~e_1 Domi_s~2
{hline 13}{c +}{hline 18}
Domi_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Domi_scale_2 {c |} {res}  0.6405   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Dominant_1 Toughminded_1 Dominant_2 Toughminded_2 if Conflict_1 == 1, sig

             {txt}{c |} Domina~1 Toughm~1 Domina~2 Toughm~2
{hline 13}{c +}{hline 36}
  Dominant_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Toughminde~1 {c |} {res}  0.4859   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
  Dominant_2 {c |} {res}  0.5467   0.4457   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
Toughminde~2 {c |} {res}  0.4058   0.5493   0.5506   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. reg Domi_scale_1 Domi_scale_2 if Conflict_1 == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       379
{txt}{hline 13}{c +}{hline 34}   F(1, 377)       = {res}   262.31
{txt}       Model {c |} {res} 10.5973542         1  10.5973542   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 15.2308481       377  .040400127   {txt}R-squared       ={res}    0.4103
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4087
{txt}       Total {c |} {res} 25.8282023       378  .068328577   {txt}Root MSE        =   {res}   .201

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Domi_scale_2 {c |}{col 14}{res}{space 2} .6183063{col 26}{space 2} .0381765{col 37}{space 1}   16.20{col 46}{space 3}0.000{col 54}{space 4} .5432407{col 67}{space 3} .6933719
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2019736{col 26}{space 2} .0225106{col 37}{space 1}    8.97{col 46}{space 3}0.000{col 54}{space 4} .1577117{col 67}{space 3} .2462356
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ***** Figure 1 is produced below after restructuring the data-file from wide to long
. 
. 
. 
. ************************************************** TESTING THE CONFLICT-SENSITIVITY HYPOTHESIS ***************************************************
. **** Tests if thinking about a peaceful future affect trait preferences in a leader in wave 1
. *** Key results reported in main text and full models in SOM.3a
. reg Comp_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,057
{txt}{hline 13}{c +}{hline 34}   F(1, 1055)      = {res}     6.34
{txt}       Model {c |} {res} .133615968         1  .133615968   {txt}Prob > F        ={res}    0.0119
{txt}    Residual {c |} {res} 22.2280058     1,055    .0210692   {txt}R-squared       ={res}    0.0060
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0050
{txt}       Total {c |} {res} 22.3616218     1,056  .021175778   {txt}Root MSE        =   {res} .14515

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Comp_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0224865{col 28}{space 2} .0089293{col 39}{space 1}    2.52{col 48}{space 3}0.012{col 56}{space 4} .0049653{col 69}{space 3} .0400077
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8824179{col 28}{space 2} .0063169{col 39}{space 1}  139.69{col 48}{space 3}0.000{col 56}{space 4} .8700227{col 69}{space 3} .8948131
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,057}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0224865{col 28}{space 2} .0089293{col 39}{space 1}    2.52{col 48}{space 3}0.012{col 56}{space 4} .0049653{col 69}{space 3} .0400077
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.1(.05).1)) ylabel(-.1(.05).1) recast(scatter) yline(0) ///
> xtitle("Peace") ytitle("Marg. Effect of Peace on Competence Importance") title("Competence") scheme(s1mono) legend(off)  name(Competence, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Warm_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,055
{txt}{hline 13}{c +}{hline 34}   F(1, 1053)      = {res}     7.82
{txt}       Model {c |} {res} .397981906         1  .397981906   {txt}Prob > F        ={res}    0.0053
{txt}    Residual {c |} {res} 53.5871122     1,053  .050889945   {txt}R-squared       ={res}    0.0074
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0064
{txt}       Total {c |} {res} 53.9850941     1,054  .051219254   {txt}Root MSE        =   {res} .22559

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Warm_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0388455{col 28}{space 2} .0138907{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0115888{col 69}{space 3} .0661021
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6990476{col 28}{space 2} .0098455{col 39}{space 1}   71.00{col 48}{space 3}0.000{col 56}{space 4} .6797287{col 69}{space 3} .7183666
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,055}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0388455{col 28}{space 2} .0138907{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0115888{col 69}{space 3} .0661021
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.1(.05).1)) ylabel(-.1(.05).1) recast(scatter) yline(0) ///
> xtitle("Peace")  ytitle("Marg. Effect of Peace on Warmth Importance") title("Warmth") scheme(s1mono) legend(off)  name(Warmth, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Domi_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,051
{txt}{hline 13}{c +}{hline 34}   F(1, 1049)      = {res}     4.41
{txt}       Model {c |} {res} .294218428         1  .294218428   {txt}Prob > F        ={res}    0.0360
{txt}    Residual {c |} {res}  70.039232     1,049  .066767619   {txt}R-squared       ={res}    0.0042
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0032
{txt}       Total {c |} {res} 70.3334504     1,050  .066984238   {txt}Root MSE        =   {res} .25839

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Domi_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0334636{col 28}{space 2} .0159412{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.0647439{col 69}{space 3}-.0021834
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .5288953{col 28}{space 2} .0113096{col 39}{space 1}   46.77{col 48}{space 3}0.000{col 56}{space 4} .5067032{col 69}{space 3} .5510873
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,051}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0334636{col 28}{space 2} .0159412{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.0647439{col 69}{space 3}-.0021834
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.1(.05).1)) ylabel(-.1(.05).1) recast(scatter) yline(0) ///
> xtitle("Peace")  ytitle("Marg. Effect of Peace on Dominance Importance") title("Dominance") scheme(s1mono) legend(off)  name(Dominance, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. *** Creates Figure 2
. graph combine Competence Warmth Dominance, scheme(s1mono) col(3) ysize(3) xsize(6)
{res}{txt}
{com}. graph export fig2.pdf, replace
{txt}{p 0 4 2}
file {bf}
fig2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. ***** Within-subjects test of conflict-sensitivity hypothesis is produced below after restructuring the data-file from wide to long
. 
. 
. 
. 
. ***************************** TESTING THE EFFECTS OF EMOTIONAL REACTIONS TO THE WAR ON LEADER TRAIT PREFERENCES **********************************
. *** Key results reported in main text and full models in SOM.4
. reg Comp_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.13
{txt}       Model {c |} {res} .500624175         7  .071517739   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 12.4404021       718  .017326465   {txt}R-squared       ={res}    0.0387
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0293
{txt}       Total {c |} {res} 12.9410263       725  .017849691   {txt}Root MSE        =   {res} .13163

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0581295{col 29}{space 2} .0267534{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1106536{col 70}{space 3}-.0056054
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0723606{col 29}{space 2} .0264822{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0203688{col 70}{space 3} .1243524
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0503068{col 29}{space 2} .0274303{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0035465{col 70}{space 3} .1041601
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0396835{col 29}{space 2}  .027631{col 40}{space 1}    1.44{col 49}{space 3}0.151{col 57}{space 4}-.0145638{col 70}{space 3} .0939307
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1065099{col 29}{space 2} .0443735{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .0193925{col 70}{space 3} .1936273
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}  .014314{col 29}{space 2}  .022299{col 40}{space 1}    0.64{col 49}{space 3}0.521{col 57}{space 4} -.029465{col 70}{space 3} .0580929
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0254169{col 29}{space 2} .0316162{col 40}{space 1}   -0.80{col 49}{space 3}0.422{col 57}{space 4}-.0874881{col 70}{space 3} .0366542
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0031561{col 29}{space 2}  .005406{col 40}{space 1}   -0.58{col 49}{space 3}0.560{col 57}{space 4}-.0137696{col 70}{space 3} .0074574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.46
{txt}       Model {c |} {res} 1.47300002         7  .210428574   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 33.8399859       718    .0471309   {txt}R-squared       ={res}    0.0417
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0324
{txt}       Total {c |} {res} 35.3129859       725  .048707567   {txt}Root MSE        =   {res}  .2171

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0771967{col 29}{space 2} .0441241{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1638244{col 70}{space 3}  .009431
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0914454{col 29}{space 2} .0436769{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} .0056957{col 70}{space 3}  .177195
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1047498{col 29}{space 2} .0452407{col 40}{space 1}    2.32{col 49}{space 3}0.021{col 57}{space 4}   .01593{col 70}{space 3} .1935697
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0477427{col 29}{space 2} .0455716{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1372123{col 70}{space 3} .0417269
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0450279{col 29}{space 2}  .073185{col 40}{space 1}    0.62{col 49}{space 3}0.539{col 57}{space 4}-.0986543{col 70}{space 3}   .18871
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1459127{col 29}{space 2} .0367775{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .0737083{col 70}{space 3}  .218117
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0462364{col 29}{space 2} .0521443{col 40}{space 1}   -0.89{col 49}{space 3}0.376{col 57}{space 4}  -.14861{col 70}{space 3} .0561372
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0490779{col 29}{space 2} .0089161{col 40}{space 1}   -5.50{col 49}{space 3}0.000{col 57}{space 4}-.0665827{col 70}{space 3}-.0315731
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Domi_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     1.25
{txt}       Model {c |} {res} .443999179         7  .063428454   {txt}Prob > F        ={res}    0.2719
{txt}    Residual {c |} {res} 36.3831826       718  .050672956   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 36.8271818       725  .050796113   {txt}Root MSE        =   {res} .22511

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0067655{col 29}{space 2} .0457521{col 40}{space 1}    0.15{col 49}{space 3}0.882{col 57}{space 4}-.0830584{col 70}{space 3} .0965894
{txt}aggressive_diff {c |}{col 17}{res}{space 2}  .099491{col 29}{space 2} .0452884{col 40}{space 1}    2.20{col 49}{space 3}0.028{col 57}{space 4} .0105775{col 70}{space 3} .1884044
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0315586{col 29}{space 2} .0469099{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.0605383{col 70}{space 3} .1236556
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0005131{col 29}{space 2} .0472531{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.0932838{col 70}{space 3} .0922576
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0718635{col 29}{space 2} .0758852{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.0771199{col 70}{space 3}  .220847
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0243516{col 29}{space 2} .0381345{col 40}{space 1}   -0.64{col 49}{space 3}0.523{col 57}{space 4}  -.09922{col 70}{space 3} .0505168
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0678296{col 29}{space 2} .0540683{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.0383212{col 70}{space 3} .1739804
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0250402{col 29}{space 2} .0092451{col 40}{space 1}    2.71{col 49}{space 3}0.007{col 57}{space 4} .0068895{col 70}{space 3} .0431908
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *** Creates Figure 3
. * Calculates observed ranges of changes (difference across waves) in fearfull and aggressive emotions in the sample
. sum fearfull_diff if include==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_d~f {c |}{res}        748   -.0581922    .2019064  -.8333333   .5555556
{txt}
{com}. sum aggressive_diff if include==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
aggressive~f {c |}{res}        749    .0140558    .1950568  -.8333333   .7777777
{txt}
{com}. 
. *Plot marginal effects for the ranges of changes in our sample
. *Requires instalation of coefplot
. *ssc install coefplot
. *Comp
. reg Comp_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.13
{txt}       Model {c |} {res} .500624175         7  .071517739   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 12.4404021       718  .017326465   {txt}R-squared       ={res}    0.0387
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0293
{txt}       Total {c |} {res} 12.9410263       725  .017849691   {txt}Root MSE        =   {res} .13163

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0581295{col 29}{space 2} .0267534{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1106536{col 70}{space 3}-.0056054
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0723606{col 29}{space 2} .0264822{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0203688{col 70}{space 3} .1243524
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0503068{col 29}{space 2} .0274303{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0035465{col 70}{space 3} .1041601
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0396835{col 29}{space 2}  .027631{col 40}{space 1}    1.44{col 49}{space 3}0.151{col 57}{space 4}-.0145638{col 70}{space 3} .0939307
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1065099{col 29}{space 2} .0443735{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .0193925{col 70}{space 3} .1936273
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}  .014314{col 29}{space 2}  .022299{col 40}{space 1}    0.64{col 49}{space 3}0.521{col 57}{space 4} -.029465{col 70}{space 3} .0580929
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0254169{col 29}{space 2} .0316162{col 40}{space 1}   -0.80{col 49}{space 3}0.422{col 57}{space 4}-.0874881{col 70}{space 3} .0366542
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0031561{col 29}{space 2}  .005406{col 40}{space 1}   -0.58{col 49}{space 3}0.560{col 57}{space 4}-.0137696{col 70}{space 3} .0074574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.fearfull_diff=(-.8333333(0.2).5555556)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.3666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0465808{col 26}{space 2} .0212673{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .0048273{col 67}{space 3} .0883344
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0349549{col 26}{space 2} .0161067{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0033331{col 67}{space 3} .0665768
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .023329{col 26}{space 2} .0111271{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0014836{col 67}{space 3} .0451745
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0117031{col 26}{space 2} .0067422{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0015336{col 67}{space 3} .0249398
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0000772{col 26}{space 2} .0049357{col 37}{space 1}    0.02{col 46}{space 3}0.988{col 54}{space 4}-.0096129{col 67}{space 3} .0097674
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0115487{col 26}{space 2} .0077798{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-.0268225{col 67}{space 3} .0037252
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0231746{col 26}{space 2} .0124076{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.0475341{col 67}{space 3} .0011849
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store comp_fearful
{txt}
{com}. 
. reg Comp_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.13
{txt}       Model {c |} {res} .500624175         7  .071517739   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 12.4404021       718  .017326465   {txt}R-squared       ={res}    0.0387
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0293
{txt}       Total {c |} {res} 12.9410263       725  .017849691   {txt}Root MSE        =   {res} .13163

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0581295{col 29}{space 2} .0267534{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1106536{col 70}{space 3}-.0056054
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0723606{col 29}{space 2} .0264822{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0203688{col 70}{space 3} .1243524
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0503068{col 29}{space 2} .0274303{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0035465{col 70}{space 3} .1041601
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0396835{col 29}{space 2}  .027631{col 40}{space 1}    1.44{col 49}{space 3}0.151{col 57}{space 4}-.0145638{col 70}{space 3} .0939307
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1065099{col 29}{space 2} .0443735{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .0193925{col 70}{space 3} .1936273
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}  .014314{col 29}{space 2}  .022299{col 40}{space 1}    0.64{col 49}{space 3}0.521{col 57}{space 4} -.029465{col 70}{space 3} .0580929
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0254169{col 29}{space 2} .0316162{col 40}{space 1}   -0.80{col 49}{space 3}0.422{col 57}{space 4}-.0874881{col 70}{space 3} .0366542
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0031561{col 29}{space 2}  .005406{col 40}{space 1}   -0.58{col 49}{space 3}0.560{col 57}{space 4}-.0137696{col 70}{space 3} .0074574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.aggressive_diff=(-.8333333(0.2).7777777)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.3666667}}
{lalign 7:8._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.5666667}}
{lalign 7:9._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.7666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0600058{col 26}{space 2} .0230719{col 37}{space 1}   -2.60{col 46}{space 3}0.009{col 54}{space 4}-.1053023{col 67}{space 3}-.0147094
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0455337{col 26}{space 2} .0179307{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.0807365{col 67}{space 3}-.0103309
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0310616{col 26}{space 2} .0129155{col 37}{space 1}   -2.40{col 46}{space 3}0.016{col 54}{space 4}-.0564182{col 67}{space 3} -.005705
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0165895{col 26}{space 2} .0082592{col 37}{space 1}   -2.01{col 46}{space 3}0.045{col 54}{space 4}-.0328045{col 67}{space 3}-.0003745
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0021174{col 26}{space 2} .0050718{col 37}{space 1}   -0.42{col 46}{space 3}0.676{col 54}{space 4}-.0120748{col 67}{space 3}   .00784
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0123548{col 26}{space 2}  .006272{col 37}{space 1}    1.97{col 46}{space 3}0.049{col 54}{space 4} .0000412{col 67}{space 3} .0246683
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0268269{col 26}{space 2}  .010443{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} .0063244{col 67}{space 3} .0473293
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  .041299{col 26}{space 2} .0153258{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4} .0112104{col 67}{space 3} .0713877
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0557711{col 26}{space 2} .0204158{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0156893{col 67}{space 3}  .095853
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store comp_aggresive
{txt}
{com}. 
. coefplot (comp_fearful, recast(line) lcolor(green) lwidth(*3) ciopts(recast(rarea) color(green%50)) ylab(-0.15(0.05)0.15) /// 
>             label("{c -(}&Delta{c )-} Fearfull emotions") yline(0, lpattern(dash)))  /// 
>                  (comp_aggresive, recast(line) lcolor(blue) lwidth(*3) ciopts(recast(rarea) color(blue%50)) ylab(-0.15(0.05)0.15) /// 
>                     label("{c -(}&Delta{c )-} Aggressive emotions")), ytitle("Predicted change in {c -(}bf:competence{c )-} preference" " ") scheme(s1mono) at
{res}{txt}
{com}.         graph save comp, replace
{txt}{p 0 4 2}
(file {bf}
comp.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:comp.gph} saved

{com}.                         
. *Warm           
. reg Warm_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.46
{txt}       Model {c |} {res} 1.47300002         7  .210428574   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 33.8399859       718    .0471309   {txt}R-squared       ={res}    0.0417
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0324
{txt}       Total {c |} {res} 35.3129859       725  .048707567   {txt}Root MSE        =   {res}  .2171

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0771967{col 29}{space 2} .0441241{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1638244{col 70}{space 3}  .009431
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0914454{col 29}{space 2} .0436769{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} .0056957{col 70}{space 3}  .177195
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1047498{col 29}{space 2} .0452407{col 40}{space 1}    2.32{col 49}{space 3}0.021{col 57}{space 4}   .01593{col 70}{space 3} .1935697
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0477427{col 29}{space 2} .0455716{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1372123{col 70}{space 3} .0417269
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0450279{col 29}{space 2}  .073185{col 40}{space 1}    0.62{col 49}{space 3}0.539{col 57}{space 4}-.0986543{col 70}{space 3}   .18871
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1459127{col 29}{space 2} .0367775{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .0737083{col 70}{space 3}  .218117
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0462364{col 29}{space 2} .0521443{col 40}{space 1}   -0.89{col 49}{space 3}0.376{col 57}{space 4}  -.14861{col 70}{space 3} .0561372
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0490779{col 29}{space 2} .0089161{col 40}{space 1}   -5.50{col 49}{space 3}0.000{col 57}{space 4}-.0665827{col 70}{space 3}-.0315731
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.fearfull_diff=(-.8333333(0.2).5555556)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.3666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0132382{col 26}{space 2} .0350761{col 37}{space 1}    0.38{col 46}{space 3}0.706{col 54}{space 4}-.0556257{col 67}{space 3} .0821021
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0022011{col 26}{space 2} .0265647{col 37}{space 1}   -0.08{col 46}{space 3}0.934{col 54}{space 4}-.0543548{col 67}{space 3} .0499526
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0176405{col 26}{space 2} .0183518{col 37}{space 1}   -0.96{col 46}{space 3}0.337{col 54}{space 4}  -.05367{col 67}{space 3} .0183891
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0330798{col 26}{space 2} .0111198{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4} -.054911{col 67}{space 3}-.0112486
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0485191{col 26}{space 2} .0081405{col 37}{space 1}   -5.96{col 46}{space 3}0.000{col 54}{space 4}-.0645011{col 67}{space 3}-.0325372
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0639585{col 26}{space 2} .0128312{col 37}{space 1}   -4.98{col 46}{space 3}0.000{col 54}{space 4}-.0891496{col 67}{space 3}-.0387674
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0793978{col 26}{space 2} .0204638{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-.1195738{col 67}{space 3}-.0392218
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store warm_fearfull
{txt}
{com}. 
. reg Warm_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.46
{txt}       Model {c |} {res} 1.47300002         7  .210428574   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 33.8399859       718    .0471309   {txt}R-squared       ={res}    0.0417
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0324
{txt}       Total {c |} {res} 35.3129859       725  .048707567   {txt}Root MSE        =   {res}  .2171

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0771967{col 29}{space 2} .0441241{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1638244{col 70}{space 3}  .009431
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0914454{col 29}{space 2} .0436769{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} .0056957{col 70}{space 3}  .177195
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1047498{col 29}{space 2} .0452407{col 40}{space 1}    2.32{col 49}{space 3}0.021{col 57}{space 4}   .01593{col 70}{space 3} .1935697
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0477427{col 29}{space 2} .0455716{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1372123{col 70}{space 3} .0417269
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0450279{col 29}{space 2}  .073185{col 40}{space 1}    0.62{col 49}{space 3}0.539{col 57}{space 4}-.0986543{col 70}{space 3}   .18871
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1459127{col 29}{space 2} .0367775{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .0737083{col 70}{space 3}  .218117
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0462364{col 29}{space 2} .0521443{col 40}{space 1}   -0.89{col 49}{space 3}0.376{col 57}{space 4}  -.14861{col 70}{space 3} .0561372
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0490779{col 29}{space 2} .0089161{col 40}{space 1}   -5.50{col 49}{space 3}0.000{col 57}{space 4}-.0665827{col 70}{space 3}-.0315731
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.aggressive_diff=(-.8333333(0.2).7777777)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.3666667}}
{lalign 7:8._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.5666667}}
{lalign 7:9._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.7666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1243505{col 26}{space 2} .0380523{col 37}{space 1}   -3.27{col 46}{space 3}0.001{col 54}{space 4}-.1990576{col 67}{space 3}-.0496434
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1060614{col 26}{space 2} .0295729{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4}-.1641211{col 67}{space 3}-.0480017
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0877724{col 26}{space 2} .0213014{col 37}{space 1}   -4.12{col 46}{space 3}0.000{col 54}{space 4}-.1295928{col 67}{space 3} -.045952
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0694833{col 26}{space 2} .0136218{col 37}{space 1}   -5.10{col 46}{space 3}0.000{col 54}{space 4}-.0962266{col 67}{space 3}  -.04274
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0511942{col 26}{space 2} .0083649{col 37}{space 1}   -6.12{col 46}{space 3}0.000{col 54}{space 4}-.0676169{col 67}{space 3}-.0347716
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0329051{col 26}{space 2} .0103443{col 37}{space 1}   -3.18{col 46}{space 3}0.002{col 54}{space 4}-.0532138{col 67}{space 3}-.0125965
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0146161{col 26}{space 2} .0172235{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-.0484306{col 67}{space 3} .0191984
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  .003673{col 26}{space 2} .0252767{col 37}{space 1}    0.15{col 46}{space 3}0.885{col 54}{space 4} -.045952{col 67}{space 3}  .053298
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0219621{col 26}{space 2} .0336717{col 37}{space 1}    0.65{col 46}{space 3}0.514{col 54}{space 4}-.0441447{col 67}{space 3} .0880688
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store warm_aggressive
{txt}
{com}. 
. coefplot (warm_fearfull, recast(line) lcolor(green) lwidth(*3) ciopts(recast(rarea) color(green%50)) ylab(-0.15(0.05)0.15) /// 
>             label("{c -(}&Delta{c )-} Fearfull emotions") yline(0, lpattern(dash)))  ///  
>                  (warm_aggressive, recast(line) lcolor(blue) lwidth(*3) ciopts(recast(rarea) color(blue%50)) ylab(-0.15(0.05)0.15) /// 
>                     label("{c -(}&Delta{c )-} Aggressive emotions")), ytitle("Predicted change in {c -(}bf:warmth{c )-} preference" " ") scheme(s1mono) at
{res}{txt}
{com}.         graph save warm, replace
{txt}{p 0 4 2}
(file {bf}
warm.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:warm.gph} saved

{com}.         
. *Domi
. reg Domi_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     1.25
{txt}       Model {c |} {res} .443999179         7  .063428454   {txt}Prob > F        ={res}    0.2719
{txt}    Residual {c |} {res} 36.3831826       718  .050672956   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 36.8271818       725  .050796113   {txt}Root MSE        =   {res} .22511

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0067655{col 29}{space 2} .0457521{col 40}{space 1}    0.15{col 49}{space 3}0.882{col 57}{space 4}-.0830584{col 70}{space 3} .0965894
{txt}aggressive_diff {c |}{col 17}{res}{space 2}  .099491{col 29}{space 2} .0452884{col 40}{space 1}    2.20{col 49}{space 3}0.028{col 57}{space 4} .0105775{col 70}{space 3} .1884044
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0315586{col 29}{space 2} .0469099{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.0605383{col 70}{space 3} .1236556
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0005131{col 29}{space 2} .0472531{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.0932838{col 70}{space 3} .0922576
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0718635{col 29}{space 2} .0758852{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.0771199{col 70}{space 3}  .220847
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0243516{col 29}{space 2} .0381345{col 40}{space 1}   -0.64{col 49}{space 3}0.523{col 57}{space 4}  -.09922{col 70}{space 3} .0505168
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0678296{col 29}{space 2} .0540683{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.0383212{col 70}{space 3} .1739804
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0250402{col 29}{space 2} .0092451{col 40}{space 1}    2.71{col 49}{space 3}0.007{col 57}{space 4} .0068895{col 70}{space 3} .0431908
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.fearfull_diff=(-.8333333(0.2).5555556)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.3666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0182964{col 26}{space 2} .0363702{col 37}{space 1}    0.50{col 46}{space 3}0.615{col 54}{space 4}-.0531083{col 67}{space 3} .0897011
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0196495{col 26}{space 2} .0275448{col 37}{space 1}    0.71{col 46}{space 3}0.476{col 54}{space 4}-.0344285{col 67}{space 3} .0737275
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0210026{col 26}{space 2} .0190289{col 37}{space 1}    1.10{col 46}{space 3}0.270{col 54}{space 4}-.0163563{col 67}{space 3} .0583615
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0223557{col 26}{space 2} .0115301{col 37}{space 1}    1.94{col 46}{space 3}0.053{col 54}{space 4} -.000281{col 67}{space 3} .0449924
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0237088{col 26}{space 2} .0084408{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0071372{col 67}{space 3} .0402804
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0250619{col 26}{space 2} .0133046{col 37}{space 1}    1.88{col 46}{space 3}0.060{col 54}{space 4}-.0010586{col 67}{space 3} .0511825
{txt}{space 10}7  {c |}{col 14}{res}{space 2}  .026415{col 26}{space 2} .0212188{col 37}{space 1}    1.24{col 46}{space 3}0.214{col 54}{space 4}-.0152433{col 67}{space 3} .0680733
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store domi_fearfull
{txt}
{com}. 
. reg Domi_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     1.25
{txt}       Model {c |} {res} .443999179         7  .063428454   {txt}Prob > F        ={res}    0.2719
{txt}    Residual {c |} {res} 36.3831826       718  .050672956   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 36.8271818       725  .050796113   {txt}Root MSE        =   {res} .22511

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0067655{col 29}{space 2} .0457521{col 40}{space 1}    0.15{col 49}{space 3}0.882{col 57}{space 4}-.0830584{col 70}{space 3} .0965894
{txt}aggressive_diff {c |}{col 17}{res}{space 2}  .099491{col 29}{space 2} .0452884{col 40}{space 1}    2.20{col 49}{space 3}0.028{col 57}{space 4} .0105775{col 70}{space 3} .1884044
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0315586{col 29}{space 2} .0469099{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.0605383{col 70}{space 3} .1236556
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0005131{col 29}{space 2} .0472531{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.0932838{col 70}{space 3} .0922576
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0718635{col 29}{space 2} .0758852{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.0771199{col 70}{space 3}  .220847
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0243516{col 29}{space 2} .0381345{col 40}{space 1}   -0.64{col 49}{space 3}0.523{col 57}{space 4}  -.09922{col 70}{space 3} .0505168
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0678296{col 29}{space 2} .0540683{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.0383212{col 70}{space 3} .1739804
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0250402{col 29}{space 2} .0092451{col 40}{space 1}    2.71{col 49}{space 3}0.007{col 57}{space 4} .0068895{col 70}{space 3} .0431908
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.aggressive_diff=(-.8333333(0.2).7777777)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.3666667}}
{lalign 7:8._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.5666667}}
{lalign 7:9._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.7666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0611827{col 26}{space 2} .0394563{col 37}{space 1}   -1.55{col 46}{space 3}0.121{col 54}{space 4}-.1386462{col 67}{space 3} .0162808
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0412845{col 26}{space 2}  .030664{col 37}{space 1}   -1.35{col 46}{space 3}0.179{col 54}{space 4}-.1014864{col 67}{space 3} .0189174
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0213863{col 26}{space 2} .0220873{col 37}{space 1}   -0.97{col 46}{space 3}0.333{col 54}{space 4}-.0647498{col 67}{space 3} .0219771
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0014881{col 26}{space 2} .0141244{col 37}{space 1}   -0.11{col 46}{space 3}0.916{col 54}{space 4}-.0292182{col 67}{space 3} .0262419
{txt}{space 10}5  {c |}{col 14}{res}{space 2}   .01841{col 26}{space 2} .0086736{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0013815{col 67}{space 3} .0354386
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0383082{col 26}{space 2}  .010726{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .0172502{col 67}{space 3} .0593662
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0582064{col 26}{space 2}  .017859{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .0231443{col 67}{space 3} .0932686
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0781046{col 26}{space 2} .0262093{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0266486{col 67}{space 3} .1295606
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0980028{col 26}{space 2}  .034914{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4}  .029457{col 67}{space 3} .1665486
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store domi_aggressive
{txt}
{com}. 
. coefplot (domi_fearfull, recast(line) lcolor(green) lwidth(*3) ciopts(recast(rarea) color(green%50)) ylab(-0.15(0.05)0.15) /// 
>             label("{c -(}&Delta{c )-} Fearfull emotions") yline(0, lpattern(dash)))  /// 
>                  (domi_aggressive, recast(line) lcolor(blue) lwidth(*3) ciopts(recast(rarea) color(blue%50)) ylab(-0.15(0.05)0.15) /// 
>                     label("{c -(}&Delta{c )-} Aggressive emotions")), ytitle("Predicted change in {c -(}bf:dominance{c )-} preference" " ")scheme(s1mono) at
{res}{txt}
{com}.         graph save domi, replace
{txt}{p 0 4 2}
(file {bf}
domi.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:domi.gph} saved

{com}.                                 
. graph combine comp.gph warm.gph domi.gph, ycommon xsize(12) ysize(3) col(3) scale(1.45) scheme(s1mono)
{res}{txt}
{com}. graph export fig3.pdf, replace
{txt}{p 0 4 2}
file {bf}
fig3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. **************************************************************************************************************************************************
. ********************************************************** SUPPLEMENTARY ANALYSES ****************************************************************
. **************************************************************************************************************************************************
. 
. ********************************************** SOM.6. ANALYSES USING SINGLE-ITEM TRAIT VARIABLES *************************************************
. *** SOM 6.a: Average trait importance across survey waves based on single-item trait measures 
. * Wave 1
. reg Competence_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       363
{txt}{hline 13}{c +}{hline 34}   F(0, 362)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 14.3027241       362  .039510287   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 14.3027241       362  .039510287   {txt}Root MSE        =   {res} .19877

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Competence_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8797061{col 26}{space 2} .0104328{col 37}{space 1}   84.32{col 46}{space 3}0.000{col 54}{space 4} .8591896{col 67}{space 3} .9002227
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Trustworthy_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       369
{txt}{hline 13}{c +}{hline 34}   F(0, 368)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 5.52318604       368  .015008658   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 5.52318604       368  .015008658   {txt}Root MSE        =   {res} .12251

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Trustworth~1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2}  .932701{col 26}{space 2} .0063776{col 37}{space 1}  146.25{col 46}{space 3}0.000{col 54}{space 4} .9201599{col 67}{space 3} .9452421
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Strong_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 9.90411453       373  .026552586   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 9.90411453       373  .026552586   {txt}Root MSE        =   {res} .16295

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Strong_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8881462{col 26}{space 2} .0084259{col 37}{space 1}  105.41{col 46}{space 3}0.000{col 54}{space 4} .8715779{col 67}{space 3} .9047144
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 22.7845154       370  .061579771   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 22.7845154       370  .061579771   {txt}Root MSE        =   {res} .24815

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Warm_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6922731{col 26}{space 2} .0128834{col 37}{space 1}   53.73{col 46}{space 3}0.000{col 54}{space 4} .6669392{col 67}{space 3} .7176071
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Generous_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 23.1495948       370  .062566472   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 23.1495948       370  .062566472   {txt}Root MSE        =   {res} .25013

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Generous_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7160827{col 26}{space 2} .0129863{col 37}{space 1}   55.14{col 46}{space 3}0.000{col 54}{space 4} .6905465{col 67}{space 3} .7416188
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Dominant_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       369
{txt}{hline 13}{c +}{hline 34}   F(0, 368)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 33.3515499       368  .090629212   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 33.3515499       368  .090629212   {txt}Root MSE        =   {res} .30105

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Dominant_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6102078{col 26}{space 2} .0156719{col 37}{space 1}   38.94{col 46}{space 3}0.000{col 54}{space 4} .5793901{col 67}{space 3} .6410254
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Toughminded_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 34.1527031       373  .091562207   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 34.1527031       373  .091562207   {txt}Root MSE        =   {res} .30259

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Toughminde~1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4465241{col 26}{space 2} .0156467{col 37}{space 1}   28.54{col 46}{space 3}0.000{col 54}{space 4} .4157573{col 67}{space 3} .4772908
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Wave 2
. reg Competence_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       373
{txt}{hline 13}{c +}{hline 34}   F(0, 372)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 12.3297588       372  .033144513   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 12.3297588       372  .033144513   {txt}Root MSE        =   {res} .18206

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Competence_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9008043{col 26}{space 2} .0094265{col 37}{space 1}   95.56{col 46}{space 3}0.000{col 54}{space 4} .8822683{col 67}{space 3} .9193402
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Trustworthy_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 9.78870927       370  .026455971   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 9.78870927       370  .026455971   {txt}Root MSE        =   {res} .16265

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Trustworth~2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9218329{col 26}{space 2} .0084445{col 37}{space 1}  109.16{col 46}{space 3}0.000{col 54}{space 4} .9052276{col 67}{space 3} .9384382
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Strong_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 9.33571006       373  .025028713   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 9.33571006       373  .025028713   {txt}Root MSE        =   {res}  .1582

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Strong_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9064171{col 26}{space 2} .0081806{col 37}{space 1}  110.80{col 46}{space 3}0.000{col 54}{space 4} .8903313{col 67}{space 3} .9225029
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       370
{txt}{hline 13}{c +}{hline 34}   F(0, 369)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 29.1673414       369  .079044286   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 29.1673414       369  .079044286   {txt}Root MSE        =   {res} .28115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Warm_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6581081{col 26}{space 2} .0146162{col 37}{space 1}   45.03{col 46}{space 3}0.000{col 54}{space 4} .6293666{col 67}{space 3} .6868496
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Generous_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 23.1495948       370  .062566472   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 23.1495948       370  .062566472   {txt}Root MSE        =   {res} .25013

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Generous_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7160827{col 26}{space 2} .0129863{col 37}{space 1}   55.14{col 46}{space 3}0.000{col 54}{space 4} .6905465{col 67}{space 3} .7416188
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Dominant_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       369
{txt}{hline 13}{c +}{hline 34}   F(0, 368)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 34.2083703       368  .092957528   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 34.2083703       368  .092957528   {txt}Root MSE        =   {res} .30489

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Dominant_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5438121{col 26}{space 2} .0158719{col 37}{space 1}   34.26{col 46}{space 3}0.000{col 54}{space 4} .5126011{col 67}{space 3} .5750231
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Toughminded_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       372
{txt}{hline 13}{c +}{hline 34}   F(0, 371)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 35.1925771       371  .094858698   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 35.1925771       371  .094858698   {txt}Root MSE        =   {res} .30799

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Toughminde~2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4977599{col 26}{space 2} .0159686{col 37}{space 1}   31.17{col 46}{space 3}0.000{col 54}{space 4} .4663595{col 67}{space 3} .5291602
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. *** SOM 6.b: Testing the conflict-sensitivity hypothesis (with single-item trait variables)
. reg Competence_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,024
{txt}{hline 13}{c +}{hline 34}   F(1, 1022)      = {res}    12.13
{txt}       Model {c |} {res} .458968535         1  .458968535   {txt}Prob > F        ={res}    0.0005
{txt}    Residual {c |} {res} 38.6799199     1,022   .03784728   {txt}R-squared       ={res}    0.0117
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0108
{txt}       Total {c |} {res} 39.1388884     1,023  .038258933   {txt}Root MSE        =   {res} .19454

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Competence_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}  .042346{col 28}{space 2} .0121601{col 39}{space 1}    3.48{col 48}{space 3}0.001{col 56}{space 4} .0184843{col 69}{space 3} .0662076
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8587459{col 28}{space 2} .0086571{col 39}{space 1}   99.20{col 48}{space 3}0.000{col 56}{space 4} .8417582{col 69}{space 3} .8757336
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model1
{txt}
{com}. 
. reg Trustworthy_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,045
{txt}{hline 13}{c +}{hline 34}   F(1, 1043)      = {res}     1.03
{txt}       Model {c |} {res} .022871706         1  .022871706   {txt}Prob > F        ={res}    0.3105
{txt}    Residual {c |} {res} 23.1684098     1,043   .02221324   {txt}R-squared       ={res}    0.0010
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 23.1912815     1,044  .022213871   {txt}Root MSE        =   {res} .14904

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} Trustworthy_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}  .009357{col 28}{space 2} .0092213{col 39}{space 1}    1.01{col 48}{space 3}0.310{col 56}{space 4}-.0087375{col 69}{space 3} .0274515
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9150579{col 28}{space 2} .0065485{col 39}{space 1}  139.74{col 48}{space 3}0.000{col 56}{space 4} .9022082{col 69}{space 3} .9279076
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model2
{txt}
{com}. 
. reg Strong_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,055
{txt}{hline 13}{c +}{hline 34}   F(1, 1053)      = {res}     1.85
{txt}       Model {c |} {res} .047997023         1  .047997023   {txt}Prob > F        ={res}    0.1745
{txt}    Residual {c |} {res} 27.3778584     1,053  .025999866   {txt}R-squared       ={res}    0.0018
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0008
{txt}       Total {c |} {res} 27.4258554     1,054  .026020736   {txt}Root MSE        =   {res} .16124

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Strong_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}   .01349{col 28}{space 2} .0099286{col 39}{space 1}    1.36{col 48}{space 3}0.175{col 56}{space 4}-.0059922{col 69}{space 3} .0329721
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8772928{col 28}{space 2} .0070239{col 39}{space 1}  124.90{col 48}{space 3}0.000{col 56}{space 4} .8635104{col 69}{space 3} .8910753
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model3
{txt}
{com}. 
. reg Warm_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,045
{txt}{hline 13}{c +}{hline 34}   F(1, 1043)      = {res}    10.75
{txt}       Model {c |} {res} .669721507         1  .669721507   {txt}Prob > F        ={res}    0.0011
{txt}    Residual {c |} {res} 65.0050234     1,043  .062325046   {txt}R-squared       ={res}    0.0102
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0092
{txt}       Total {c |} {res} 65.6747449     1,044  .062906844   {txt}Root MSE        =   {res} .24965

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Warm_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0506319{col 28}{space 2} .0154457{col 39}{space 1}    3.28{col 48}{space 3}0.001{col 56}{space 4} .0203236{col 69}{space 3} .0809401
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6836538{col 28}{space 2} .0109479{col 39}{space 1}   62.45{col 48}{space 3}0.000{col 56}{space 4} .6621715{col 69}{space 3} .7051362
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model4
{txt}
{com}. 
. reg Generous_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,045
{txt}{hline 13}{c +}{hline 34}   F(1, 1043)      = {res}     2.86
{txt}       Model {c |} {res} .170818797         1  .170818797   {txt}Prob > F        ={res}    0.0911
{txt}    Residual {c |} {res} 62.2984504     1,043  .059730058   {txt}R-squared       ={res}    0.0027
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0018
{txt}       Total {c |} {res} 62.4692692     1,044  .059836465   {txt}Root MSE        =   {res}  .2444

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Generous_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0255708{col 28}{space 2} .0151207{col 39}{space 1}    1.69{col 48}{space 3}0.091{col 56}{space 4}-.0040997{col 69}{space 3} .0552414
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .7150641{col 28}{space 2} .0107175{col 39}{space 1}   66.72{col 48}{space 3}0.000{col 56}{space 4} .6940337{col 69}{space 3} .7360945
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model5
{txt}
{com}. 
. reg Dominant_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,037
{txt}{hline 13}{c +}{hline 34}   F(1, 1035)      = {res}     4.57
{txt}       Model {c |} {res} .406655908         1  .406655908   {txt}Prob > F        ={res}    0.0327
{txt}    Residual {c |} {res} 92.0096065     1,035  .088898171   {txt}R-squared       ={res}    0.0044
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0034
{txt}       Total {c |} {res} 92.4162624     1,036  .089204886   {txt}Root MSE        =   {res} .29816

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Dominant_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0396076{col 28}{space 2} .0185187{col 39}{space 1}   -2.14{col 48}{space 3}0.033{col 56}{space 4}-.0759462{col 69}{space 3} -.003269
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6140351{col 28}{space 2}  .013164{col 39}{space 1}   46.65{col 48}{space 3}0.000{col 56}{space 4} .5882039{col 69}{space 3} .6398663
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model6
{txt}
{com}. 
. reg Toughminded_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,042
{txt}{hline 13}{c +}{hline 34}   F(1, 1040)      = {res}     2.35
{txt}       Model {c |} {res} .208266783         1  .208266783   {txt}Prob > F        ={res}    0.1255
{txt}    Residual {c |} {res} 92.1447924     1,040  .088600762   {txt}R-squared       ={res}    0.0023
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0013
{txt}       Total {c |} {res} 92.3530592     1,041  .088715715   {txt}Root MSE        =   {res} .29766

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} Toughminded_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0282754{col 28}{space 2} .0184424{col 39}{space 1}   -1.53{col 48}{space 3}0.126{col 56}{space 4} -.064464{col 69}{space 3} .0079132
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .4470135{col 28}{space 2} .0130658{col 39}{space 1}   34.21{col 48}{space 3}0.000{col 56}{space 4} .4213752{col 69}{space 3} .4726518
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo SOM3b_model7
{txt}
{com}. 
. etable, estimates(SOM3b_model1 SOM3b_model2 SOM3b_model3 SOM3b_model4 SOM3b_model5 SOM3b_model6 SOM3b_model7) mstat(N) mstat(r2_a) showstars showstarsnote export(SOM3b.docx)
{res}
{smcl}
{reset}{...}
{hline 41}{c -}{hline 8}{c -}{hline 3}{c -}{hline 9}{c -}{hline 3}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 9}{c -}{hline 3}
{space 41} Competence_1 Trustworthy_1 {space 1}Strong_1{space 1} {space 2}Warm_1{space 2} Generous_1 Dominant_1 Toughminded_1
{hline 41}{c -}{hline 8}{c -}{hline 3}{c -}{hline 9}{c -}{hline 3}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 9}{c -}{hline 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 3}{result:0.042} {result:**}{space 1} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 1}{result:(0.012)} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
Intercept{space 32} {space 3}{result:0.859} {result:**}{space 1} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 1}{result:(0.009)} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 8} {space 3} {space 4}{result:0.009} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 2}{result:(0.009)} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
Intercept{space 32} {space 8} {space 3} {space 4}{result:0.915} {result:**}{space 1} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 2}{result:(0.007)} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 8} {space 3} {space 9} {space 3} {space 2}{result:0.013} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {result:(0.010)} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
Intercept{space 32} {space 8} {space 3} {space 9} {space 3} {space 2}{result:0.877} {result:**} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {result:(0.007)} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 2}{result:0.051} {result:**} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {result:(0.015)} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
Intercept{space 32} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 2}{result:0.684} {result:**} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {result:(0.011)} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 2}{result:0.026} {space 2} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {result:(0.015)} {space 2} {space 7} {space 2} {space 9} {space 3}
Intercept{space 32} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 2}{result:0.715} {result:**} {space 7} {space 2} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {result:(0.011)} {space 2} {space 7} {space 2} {space 9} {space 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 1}{result:-0.040} {result:*}{space 1} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {result:(0.019)} {space 2} {space 9} {space 3}
Intercept{space 32} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 2}{result:0.614} {result:**} {space 9} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {result:(0.013)} {space 2} {space 9} {space 3}
RECODE of w1_leader_exp_condition (Split) {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 9} {space 3}
  Peace, future{space 26} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 3}{result:-0.028} {space 3}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 2}{result:(0.018)} {space 3}
Intercept{space 32} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 4}{result:0.447} {result:**}{space 1}
{space 41} {space 8} {space 3} {space 9} {space 3} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 7} {space 2} {space 2}{result:(0.013)} {space 3}
Number of observations{space 19} {space 4}{result:1024} {space 3} {space 5}{result:1045} {space 3} {space 3}{result:1055} {space 2} {space 3}{result:1045} {space 2} {space 3}{result:1045} {space 2} {space 3}{result:1037} {space 2} {space 5}{result:1042} {space 3}
Adjusted R-squared{space 23} {space 4}{result:0.01} {space 3} {space 5}{result:0.00} {space 3} {space 3}{result:0.00} {space 2} {space 3}{result:0.01} {space 2} {space 3}{result:0.00} {space 2} {space 3}{result:0.00} {space 2} {space 5}{result:0.00} {space 3}
{hline 41}{c -}{hline 8}{c -}{hline 3}{c -}{hline 9}{c -}{hline 3}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 7}{c -}{hline 2}{c -}{hline 9}{c -}{hline 3}
{p}** p<.01, * p<.05{p_end}
{res}{txt}{p 0 1 2}
(collection {res:ETable} exported to file {browse "C:/Users/au206393/OneDrive - Aarhus universitet/Desktop/PSRM acceptance log-file/SOM3b.docx":SOM3b.docx})
{p_end}

{com}. 
. 
. 
. *** SOM 6.c: Testing the role of emotional reactions (with single-item trait variables)
. reg Competence_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       709
{txt}{hline 13}{c +}{hline 34}   F(7, 701)       = {res}     1.88
{txt}       Model {c |} {res} .511978013         7  .073139716   {txt}Prob > F        ={res}    0.0696
{txt}    Residual {c |} {res}  27.226699       701  .038839799   {txt}R-squared       ={res}    0.0185
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0087
{txt}       Total {c |} {res}  27.738677       708  .039178922   {txt}Root MSE        =   {res} .19708

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Competence_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0820309{col 29}{space 2} .0405701{col 40}{space 1}   -2.02{col 49}{space 3}0.044{col 57}{space 4}-.1616844{col 70}{space 3}-.0023775
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0246926{col 29}{space 2} .0397871{col 40}{space 1}    0.62{col 49}{space 3}0.535{col 57}{space 4}-.0534236{col 70}{space 3} .1028088
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2}  .073109{col 29}{space 2} .0418207{col 40}{space 1}    1.75{col 49}{space 3}0.081{col 57}{space 4}-.0089998{col 70}{space 3} .1552177
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0326776{col 29}{space 2} .0419613{col 40}{space 1}    0.78{col 49}{space 3}0.436{col 57}{space 4}-.0497073{col 70}{space 3} .1150625
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1071831{col 29}{space 2} .0667399{col 40}{space 1}    1.61{col 49}{space 3}0.109{col 57}{space 4} -.023851{col 70}{space 3} .2382173
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .0255527{col 29}{space 2} .0335915{col 40}{space 1}    0.76{col 49}{space 3}0.447{col 57}{space 4}-.0403992{col 70}{space 3} .0915047
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0486822{col 29}{space 2} .0480928{col 40}{space 1}   -1.01{col 49}{space 3}0.312{col 57}{space 4}-.1431054{col 70}{space 3} .0457411
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -.002027{col 29}{space 2} .0082118{col 40}{space 1}   -0.25{col 49}{space 3}0.805{col 57}{space 4}-.0181497{col 70}{space 3} .0140958
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Trustworthy_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       717
{txt}{hline 13}{c +}{hline 34}   F(7, 709)       = {res}     3.38
{txt}       Model {c |} {res} .538074039         7   .07686772   {txt}Prob > F        ={res}    0.0015
{txt}    Residual {c |} {res} 16.1267334       709  .022745745   {txt}R-squared       ={res}    0.0323
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0227
{txt}       Total {c |} {res} 16.6648074       716  .023274871   {txt}Root MSE        =   {res} .15082

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Trustworthy_d~f{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0525905{col 29}{space 2} .0308852{col 40}{space 1}   -1.70{col 49}{space 3}0.089{col 57}{space 4}-.1132279{col 70}{space 3} .0080469
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0782978{col 29}{space 2} .0305555{col 40}{space 1}    2.56{col 49}{space 3}0.011{col 57}{space 4} .0183076{col 70}{space 3} .1382879
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2}  .051061{col 29}{space 2} .0317049{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4}-.0111858{col 70}{space 3} .1133078
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0036653{col 29}{space 2} .0319384{col 40}{space 1}   -0.11{col 49}{space 3}0.909{col 57}{space 4}-.0663705{col 70}{space 3} .0590399
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2}  .147509{col 29}{space 2} .0517147{col 40}{space 1}    2.85{col 49}{space 3}0.004{col 57}{space 4} .0459767{col 70}{space 3} .2490412
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .0268788{col 29}{space 2} .0257113{col 40}{space 1}    1.05{col 49}{space 3}0.296{col 57}{space 4}-.0236007{col 70}{space 3} .0773583
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0378519{col 29}{space 2} .0364933{col 40}{space 1}    1.04{col 49}{space 3}0.300{col 57}{space 4}-.0337959{col 70}{space 3} .1094997
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0135873{col 29}{space 2} .0062387{col 40}{space 1}   -2.18{col 49}{space 3}0.030{col 57}{space 4} -.025836{col 70}{space 3}-.0013387
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Strong_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       725
{txt}{hline 13}{c +}{hline 34}   F(7, 717)       = {res}     3.98
{txt}       Model {c |} {res}  .84250916         7  .120358451   {txt}Prob > F        ={res}    0.0003
{txt}    Residual {c |} {res} 21.6568776       717   .03020485   {txt}R-squared       ={res}    0.0374
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0280
{txt}       Total {c |} {res} 22.4993868       724  .031076501   {txt}Root MSE        =   {res}  .1738

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Strong_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0497466{col 29}{space 2} .0353671{col 40}{space 1}   -1.41{col 49}{space 3}0.160{col 57}{space 4} -.119182{col 70}{space 3} .0196887
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0978924{col 29}{space 2} .0352371{col 40}{space 1}    2.78{col 49}{space 3}0.006{col 57}{space 4} .0287121{col 70}{space 3} .1670728
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0480876{col 29}{space 2} .0362421{col 40}{space 1}    1.33{col 49}{space 3}0.185{col 57}{space 4}-.0230657{col 70}{space 3} .1192408
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0913268{col 29}{space 2} .0369648{col 40}{space 1}    2.47{col 49}{space 3}0.014{col 57}{space 4} .0187546{col 70}{space 3}  .163899
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0855933{col 29}{space 2} .0586684{col 40}{space 1}    1.46{col 49}{space 3}0.145{col 57}{space 4}-.0295892{col 70}{space 3} .2007758
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0029368{col 29}{space 2} .0294678{col 40}{space 1}   -0.10{col 49}{space 3}0.921{col 57}{space 4}-.0607903{col 70}{space 3} .0549166
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0591288{col 29}{space 2} .0420704{col 40}{space 1}   -1.41{col 49}{space 3}0.160{col 57}{space 4}-.1417246{col 70}{space 3} .0234671
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0057704{col 29}{space 2} .0071378{col 40}{space 1}    0.81{col 49}{space 3}0.419{col 57}{space 4}-.0082431{col 70}{space 3}  .019784
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       716
{txt}{hline 13}{c +}{hline 34}   F(7, 708)       = {res}     3.76
{txt}       Model {c |} {res} 1.61252579         7  .230360828   {txt}Prob > F        ={res}    0.0005
{txt}    Residual {c |} {res} 43.4207976       708   .06132881   {txt}R-squared       ={res}    0.0358
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0263
{txt}       Total {c |} {res} 45.0333234       715  .062983669   {txt}Root MSE        =   {res} .24765

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Warm_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.1045381{col 29}{space 2} .0505709{col 40}{space 1}   -2.07{col 49}{space 3}0.039{col 57}{space 4}-.2038249{col 70}{space 3}-.0052513
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0844083{col 29}{space 2} .0501873{col 40}{space 1}    1.68{col 49}{space 3}0.093{col 57}{space 4}-.0141255{col 70}{space 3} .1829421
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1436926{col 29}{space 2} .0525139{col 40}{space 1}    2.74{col 49}{space 3}0.006{col 57}{space 4}  .040591{col 70}{space 3} .2467942
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0209114{col 29}{space 2} .0524423{col 40}{space 1}   -0.40{col 49}{space 3}0.690{col 57}{space 4}-.1238724{col 70}{space 3} .0820496
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0618101{col 29}{space 2}  .084831{col 40}{space 1}    0.73{col 49}{space 3}0.466{col 57}{space 4}-.1047402{col 70}{space 3} .2283605
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1390536{col 29}{space 2} .0434035{col 40}{space 1}    3.20{col 49}{space 3}0.001{col 57}{space 4} .0538386{col 70}{space 3} .2242686
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0162804{col 29}{space 2} .0603714{col 40}{space 1}    0.27{col 49}{space 3}0.787{col 57}{space 4} -.102248{col 70}{space 3} .1348087
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0437774{col 29}{space 2} .0102851{col 40}{space 1}   -4.26{col 49}{space 3}0.000{col 57}{space 4}-.0639704{col 70}{space 3}-.0235845
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Generous_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       720
{txt}{hline 13}{c +}{hline 34}   F(7, 712)       = {res}     2.56
{txt}       Model {c |} {res} 1.20632126         7  .172331609   {txt}Prob > F        ={res}    0.0131
{txt}    Residual {c |} {res} 47.9538226       712  .067350874   {txt}R-squared       ={res}    0.0245
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0149
{txt}       Total {c |} {res} 49.1601439       719   .06837294   {txt}Root MSE        =   {res} .25952

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Generous_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0482396{col 29}{space 2} .0533254{col 40}{space 1}   -0.90{col 49}{space 3}0.366{col 57}{space 4}-.1529335{col 70}{space 3} .0564543
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0826283{col 29}{space 2} .0523894{col 40}{space 1}    1.58{col 49}{space 3}0.115{col 57}{space 4}-.0202279{col 70}{space 3} .1854846
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0848324{col 29}{space 2} .0543814{col 40}{space 1}    1.56{col 49}{space 3}0.119{col 57}{space 4}-.0219346{col 70}{space 3} .1915994
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0578568{col 29}{space 2} .0549396{col 40}{space 1}   -1.05{col 49}{space 3}0.293{col 57}{space 4}-.1657198{col 70}{space 3} .0500063
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2}  .018716{col 29}{space 2} .0875917{col 40}{space 1}    0.21{col 49}{space 3}0.831{col 57}{space 4}-.1532528{col 70}{space 3} .1906849
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1319676{col 29}{space 2} .0441594{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0452694{col 70}{space 3} .2186657
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0928652{col 29}{space 2}  .062403{col 40}{space 1}   -1.49{col 49}{space 3}0.137{col 57}{space 4} -.215381{col 70}{space 3} .0296506
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0509659{col 29}{space 2} .0107097{col 40}{space 1}   -4.76{col 49}{space 3}0.000{col 57}{space 4}-.0719922{col 70}{space 3}-.0299395
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Dominance_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       709
{txt}{hline 13}{c +}{hline 34}   F(7, 701)       = {res}     0.75
{txt}       Model {c |} {res}  .41743423         7  .059633461   {txt}Prob > F        ={res}    0.6280
{txt}    Residual {c |} {res} 55.5947869       701  .079307827   {txt}R-squared       ={res}    0.0075
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0025
{txt}       Total {c |} {res} 56.0122212       708  .079113307   {txt}Root MSE        =   {res} .28162

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} Dominance_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0079294{col 29}{space 2} .0576976{col 40}{space 1}   -0.14{col 49}{space 3}0.891{col 57}{space 4}-.1212102{col 70}{space 3} .1053515
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0016055{col 29}{space 2} .0576353{col 40}{space 1}    0.03{col 49}{space 3}0.978{col 57}{space 4}-.1115529{col 70}{space 3}  .114764
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1055763{col 29}{space 2} .0596606{col 40}{space 1}    1.77{col 49}{space 3}0.077{col 57}{space 4}-.0115587{col 70}{space 3} .2227112
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0196567{col 29}{space 2} .0606294{col 40}{space 1}   -0.32{col 49}{space 3}0.746{col 57}{space 4}-.1386936{col 70}{space 3} .0993802
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1119102{col 29}{space 2} .0961817{col 40}{space 1}    1.16{col 49}{space 3}0.245{col 57}{space 4}-.0769286{col 70}{space 3}  .300749
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0079579{col 29}{space 2} .0487477{col 40}{space 1}   -0.16{col 49}{space 3}0.870{col 57}{space 4}-.1036668{col 70}{space 3}  .087751
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0270551{col 29}{space 2} .0686863{col 40}{space 1}    0.39{col 49}{space 3}0.694{col 57}{space 4}-.1078004{col 70}{space 3} .1619106
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -.036551{col 29}{space 2} .0116822{col 40}{space 1}   -3.13{col 49}{space 3}0.002{col 57}{space 4}-.0594874{col 70}{space 3}-.0136146
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Toughminded_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       721
{txt}{hline 13}{c +}{hline 34}   F(7, 713)       = {res}     2.11
{txt}       Model {c |} {res} 1.20497034         7   .17213862   {txt}Prob > F        ={res}    0.0405
{txt}    Residual {c |} {res} 58.1900809       713  .081613017   {txt}R-squared       ={res}    0.0203
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0107
{txt}       Total {c |} {res} 59.3950513       720  .082493127   {txt}Root MSE        =   {res} .28568

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Toughminded_d~f{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0002145{col 29}{space 2} .0582912{col 40}{space 1}    0.00{col 49}{space 3}0.997{col 57}{space 4}-.1142284{col 70}{space 3} .1146575
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .1822095{col 29}{space 2}  .057533{col 40}{space 1}    3.17{col 49}{space 3}0.002{col 57}{space 4} .0692553{col 70}{space 3} .2951638
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2}-.0252569{col 29}{space 2} .0596021{col 40}{space 1}   -0.42{col 49}{space 3}0.672{col 57}{space 4}-.1422735{col 70}{space 3} .0917598
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}  .041708{col 29}{space 2} .0601011{col 40}{space 1}    0.69{col 49}{space 3}0.488{col 57}{space 4}-.0762883{col 70}{space 3} .1597043
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0638153{col 29}{space 2} .0972792{col 40}{space 1}    0.66{col 49}{space 3}0.512{col 57}{space 4}-.1271726{col 70}{space 3} .2548032
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0183312{col 29}{space 2} .0486229{col 40}{space 1}   -0.38{col 49}{space 3}0.706{col 57}{space 4}-.1137924{col 70}{space 3} .0771299
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .1002133{col 29}{space 2} .0701895{col 40}{space 1}    1.43{col 49}{space 3}0.154{col 57}{space 4}-.0375895{col 70}{space 3} .2380161
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0833837{col 29}{space 2}   .01177{col 40}{space 1}    7.08{col 49}{space 3}0.000{col 57}{space 4} .0602758{col 70}{space 3} .1064917
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. ********************************************** SOM.7. ANALYSES USING PCA FACTOR SCORES AS TRAIT VARIABLES *************************************************
. 
. *** SOM 7.a: Testing the Conflict-Sensitivity Hypothesis and effect of emotional reactions using factor score variables for trait dimensions
. ** Between-respondent analyses
. reg Comp_PCA_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       988
{txt}{hline 13}{c +}{hline 34}   F(1, 986)       = {res}     3.54
{txt}       Model {c |} {res}  3.5308171         1   3.5308171   {txt}Prob > F        ={res}    0.0602
{txt}    Residual {c |} {res} 983.469185       986   .99743325   {txt}R-squared       ={res}    0.0036
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0026
{txt}       Total {c |} {res} 987.000002       987           1   {txt}Root MSE        =   {res} .99872

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Comp_PCA_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .1195807{col 28}{space 2} .0635573{col 39}{space 1}    1.88{col 48}{space 3}0.060{col 56}{space 4}-.0051424{col 69}{space 3} .2443039
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0608797{col 28}{space 2} .0453493{col 39}{space 1}   -1.34{col 48}{space 3}0.180{col 56}{space 4} -.149872{col 69}{space 3} .0281127
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:988}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .1195807{col 28}{space 2} .0635573{col 39}{space 1}    1.88{col 48}{space 3}0.060{col 56}{space 4}-.0051424{col 69}{space 3} .2443039
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.3(.1).3)) ylabel(-.3(.1).3) recast(scatter) yline(0) ///
> xtitle("Peace") ytitle("Marg. Effect of Peace on Competence (PCA factor score)") title("Competence (PCA factor score)") scheme(s1mono) legend(off)  name(Competence_PCA, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Warm_PCA_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       988
{txt}{hline 13}{c +}{hline 34}   F(1, 986)       = {res}     5.42
{txt}       Model {c |} {res} 5.39956244         1  5.39956244   {txt}Prob > F        ={res}    0.0201
{txt}    Residual {c |} {res} 981.600435       986  .995537967   {txt}R-squared       ={res}    0.0055
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0045
{txt}       Total {c |} {res} 986.999998       987  .999999998   {txt}Root MSE        =   {res} .99777

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Warm_PCA_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .1478778{col 28}{space 2} .0634969{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .0232732{col 69}{space 3} .2724824
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -.075286{col 28}{space 2} .0453062{col 39}{space 1}   -1.66{col 48}{space 3}0.097{col 56}{space 4}-.1641937{col 69}{space 3} .0136218
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:988}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .1478778{col 28}{space 2} .0634969{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .0232732{col 69}{space 3} .2724824
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.3(.1).3)) ylabel(-.3(.1).3) recast(scatter) yline(0) ///
> xtitle("Peace")  ytitle("Marg. Effect of Peace on Warmth (PCA factor score)") title("Warmth (PCA factor score)") scheme(s1mono) legend(off)  name(Warmth_PCA, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Domi_PCA_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       988
{txt}{hline 13}{c +}{hline 34}   F(1, 986)       = {res}     5.89
{txt}       Model {c |} {res} 5.86408685         1  5.86408685   {txt}Prob > F        ={res}    0.0154
{txt}    Residual {c |} {res} 981.135917       986  .995066853   {txt}R-squared       ={res}    0.0059
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0049
{txt}       Total {c |} {res} 987.000004       987           1   {txt}Root MSE        =   {res} .99753

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Domi_PCA_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.1541075{col 28}{space 2} .0634819{col 39}{space 1}   -2.43{col 48}{space 3}0.015{col 56}{space 4}-.2786826{col 69}{space 3}-.0295324
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0784576{col 28}{space 2} .0452955{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.0104291{col 69}{space 3} .1673443
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:988}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.1541075{col 28}{space 2} .0634819{col 39}{space 1}   -2.43{col 48}{space 3}0.015{col 56}{space 4}-.2786826{col 69}{space 3}-.0295324
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.3(.1).3)) ylabel(-.3(.1).3) recast(scatter) yline(0) ///
> xtitle("Peace")  ytitle("Marg. Effect of Peace on Dominance (PCA factor score)") title("Dominance (PCA factor score)") scheme(s1mono) legend(off)  name(Dominance_PCA, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. * Creates common figure (not displayed in SOM.7)
. graph combine Competence_PCA Warmth_PCA Dominance_PCA, scheme(s1mono) col(3) ysize(3) xsize(6)
{res}{txt}
{com}. graph export fig2_PCA.pdf, replace
{txt}{p 0 4 2}
file {bf}
fig2_PCA.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. ** Within-respondent analyses: see below after dataset is re-shaped to long format
. 
. 
. *** SOM. 7.b: Testing the role of emotional reactions to the war using factor score trait variables
. reg Comp_PCA_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include_PCA==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       683
{txt}{hline 13}{c +}{hline 34}   F(7, 675)       = {res}     4.23
{txt}       Model {c |} {res} 25.1549777         7  3.59356824   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 573.196918       675   .84918062   {txt}R-squared       ={res}    0.0420
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0321
{txt}       Total {c |} {res} 598.351896       682  .877348821   {txt}Root MSE        =   {res} .92151

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Comp_PCA_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}  -.36725{col 29}{space 2} .1938587{col 40}{space 1}   -1.89{col 49}{space 3}0.059{col 57}{space 4}-.7478886{col 70}{space 3} .0133885
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .4766669{col 29}{space 2} .1914833{col 40}{space 1}    2.49{col 49}{space 3}0.013{col 57}{space 4} .1006924{col 70}{space 3} .8526414
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .5060609{col 29}{space 2} .2007945{col 40}{space 1}    2.52{col 49}{space 3}0.012{col 57}{space 4}  .111804{col 70}{space 3} .9003177
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .3134092{col 29}{space 2} .2031848{col 40}{space 1}    1.54{col 49}{space 3}0.123{col 57}{space 4}-.0855409{col 70}{space 3} .7123594
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .7697779{col 29}{space 2}  .328259{col 40}{space 1}    2.35{col 49}{space 3}0.019{col 57}{space 4} .1252464{col 70}{space 3} 1.414309
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1507477{col 29}{space 2} .1640946{col 40}{space 1}    0.92{col 49}{space 3}0.359{col 57}{space 4}-.1714495{col 70}{space 3} .4729449
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0235283{col 29}{space 2} .2386548{col 40}{space 1}    0.10{col 49}{space 3}0.921{col 57}{space 4}-.4450667{col 70}{space 3} .4921234
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0873647{col 29}{space 2} .0391463{col 40}{space 1}   -2.23{col 49}{space 3}0.026{col 57}{space 4} -.164228{col 70}{space 3}-.0105015
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_PCA_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include_PCA==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       683
{txt}{hline 13}{c +}{hline 34}   F(7, 675)       = {res}     3.38
{txt}       Model {c |} {res} 19.6506272         7  2.80723245   {txt}Prob > F        ={res}    0.0015
{txt}    Residual {c |} {res} 560.722134       675  .830699458   {txt}R-squared       ={res}    0.0339
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0238
{txt}       Total {c |} {res} 580.372761       682  .850986453   {txt}Root MSE        =   {res} .91143

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Warm_PCA_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.2765789{col 29}{space 2} .1917376{col 40}{space 1}   -1.44{col 49}{space 3}0.150{col 57}{space 4}-.6530526{col 70}{space 3} .0998949
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .2124068{col 29}{space 2} .1893882{col 40}{space 1}    1.12{col 49}{space 3}0.262{col 57}{space 4}-.1594539{col 70}{space 3} .5842675
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .5518197{col 29}{space 2} .1985974{col 40}{space 1}    2.78{col 49}{space 3}0.006{col 57}{space 4} .1618766{col 70}{space 3} .9417627
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.1436557{col 29}{space 2} .2009616{col 40}{space 1}   -0.71{col 49}{space 3}0.475{col 57}{space 4}-.5382408{col 70}{space 3} .2509293
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .2350241{col 29}{space 2} .3246673{col 40}{space 1}    0.72{col 49}{space 3}0.469{col 57}{space 4}-.4024552{col 70}{space 3} .8725034
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .5211665{col 29}{space 2} .1622991{col 40}{space 1}    3.21{col 49}{space 3}0.001{col 57}{space 4} .2024947{col 70}{space 3} .8398383
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.1254654{col 29}{space 2} .2360435{col 40}{space 1}   -0.53{col 49}{space 3}0.595{col 57}{space 4}-.5889333{col 70}{space 3} .3380024
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0162903{col 29}{space 2}  .038718{col 40}{space 1}   -0.42{col 49}{space 3}0.674{col 57}{space 4}-.0923125{col 70}{space 3} .0597319
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Domi_PCA_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include_PCA==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       683
{txt}{hline 13}{c +}{hline 34}   F(7, 675)       = {res}     1.58
{txt}       Model {c |} {res} 8.22526711         7  1.17503816   {txt}Prob > F        ={res}    0.1390
{txt}    Residual {c |} {res} 502.920666       675  .745067653   {txt}R-squared       ={res}    0.0161
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0059
{txt}       Total {c |} {res} 511.145933       682   .74948084   {txt}Root MSE        =   {res} .86317

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Domi_PCA_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0987235{col 29}{space 2} .1815863{col 40}{space 1}    0.54{col 49}{space 3}0.587{col 57}{space 4}-.2578185{col 70}{space 3} .4552654
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .3819084{col 29}{space 2} .1793613{col 40}{space 1}    2.13{col 49}{space 3}0.034{col 57}{space 4} .0297353{col 70}{space 3} .7340816
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0433761{col 29}{space 2}  .188083{col 40}{space 1}    0.23{col 49}{space 3}0.818{col 57}{space 4} -.325922{col 70}{space 3} .4126741
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .1620485{col 29}{space 2}  .190322{col 40}{space 1}    0.85{col 49}{space 3}0.395{col 57}{space 4}-.2116458{col 70}{space 3} .5357428
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .2895549{col 29}{space 2} .3074783{col 40}{space 1}    0.94{col 49}{space 3}0.347{col 57}{space 4}-.3141741{col 70}{space 3} .8932838
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0105256{col 29}{space 2} .1537064{col 40}{space 1}   -0.07{col 49}{space 3}0.945{col 57}{space 4}-.3123258{col 70}{space 3} .2912746
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}  .331105{col 29}{space 2} .2235466{col 40}{space 1}    1.48{col 49}{space 3}0.139{col 57}{space 4}-.1078252{col 70}{space 3} .7700353
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0707088{col 29}{space 2} .0366681{col 40}{space 1}    1.93{col 49}{space 3}0.054{col 57}{space 4}-.0012885{col 70}{space 3} .1427061
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ****************************** SOM.8. ANALYSES OF RELATIVE IMPORTANCE USING ALL AVAILABLE RESPONDENTS IN WAVE 1 *************************************************************
. *** SOM 8: Testing the relative importance of leader competence, warmth and dominance in Wave 1 using all available respondents
. reg Comp_scale_1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       528
{txt}{hline 13}{c +}{hline 34}   F(0, 527)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res}  13.078971       527  .024817782   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res}  13.078971       527  .024817782   {txt}Root MSE        =   {res} .15754

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8824179{col 26}{space 2} .0068559{col 37}{space 1}  128.71{col 46}{space 3}0.000{col 54}{space 4} .8689497{col 67}{space 3} .8958862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       525
{txt}{hline 13}{c +}{hline 34}   F(0, 524)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 29.2134109       524  .055750784   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 29.2134109       524  .055750784   {txt}Root MSE        =   {res} .23612

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6990476{col 26}{space 2} .0103049{col 37}{space 1}   67.84{col 46}{space 3}0.000{col 54}{space 4} .6788035{col 67}{space 3} .7192917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       522
{txt}{hline 13}{c +}{hline 34}   F(0, 521)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 35.6961065       521    .0685146   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 35.6961065       521    .0685146   {txt}Root MSE        =   {res} .26175

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5288953{col 26}{space 2} .0114566{col 37}{space 1}   46.17{col 46}{space 3}0.000{col 54}{space 4} .5063884{col 67}{space 3} .5514021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Comp_scale_1 if Context== 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       528
{txt}{hline 13}{c +}{hline 34}   F(0, 527)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res}  13.078971       527  .024817782   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res}  13.078971       527  .024817782   {txt}Root MSE        =   {res} .15754

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8824179{col 26}{space 2} .0068559{col 37}{space 1}  128.71{col 46}{space 3}0.000{col 54}{space 4} .8689497{col 67}{space 3} .8958862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, level(95)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:528}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8824179{col 26}{space 2} .0068559{col 37}{space 1}  128.71{col 46}{space 3}0.000{col 54}{space 4} .8689497{col 67}{space 3} .8958862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Competence") ytitle("Competence Importance") title("Competence") legend(off) scheme(s1mono) name(Comp_war_mean_SOM4, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Warm_scale_1 if Context== 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       525
{txt}{hline 13}{c +}{hline 34}   F(0, 524)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 29.2134109       524  .055750784   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 29.2134109       524  .055750784   {txt}Root MSE        =   {res} .23612

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6990476{col 26}{space 2} .0103049{col 37}{space 1}   67.84{col 46}{space 3}0.000{col 54}{space 4} .6788035{col 67}{space 3} .7192917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, level(95)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:525}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6990476{col 26}{space 2} .0103049{col 37}{space 1}   67.84{col 46}{space 3}0.000{col 54}{space 4} .6788035{col 67}{space 3} .7192917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Warmth") ytitle("Warmth Importance") title("Warmth") legend(off) scheme(s1mono) name(Warm_war_mean_SOM4, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Domi_scale_1 if Context== 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       522
{txt}{hline 13}{c +}{hline 34}   F(0, 521)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 35.6961065       521    .0685146   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 35.6961065       521    .0685146   {txt}Root MSE        =   {res} .26175

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5288953{col 26}{space 2} .0114566{col 37}{space 1}   46.17{col 46}{space 3}0.000{col 54}{space 4} .5063884{col 67}{space 3} .5514021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, level(95)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:522}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5288953{col 26}{space 2} .0114566{col 37}{space 1}   46.17{col 46}{space 3}0.000{col 54}{space 4} .5063884{col 67}{space 3} .5514021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Dominance") ytitle("Dominance Importance") title("Dominance") legend(off) scheme(s1mono) name(Domi_war_mean_SOM4, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. graph combine Comp_war_mean_SOM4 Warm_war_mean_SOM4 Domi_war_mean_SOM4, scheme(s1mono) cols(3)
{res}{txt}
{com}. 
. 
. 
. *************************************************** SOM.9. ASSESSMENT OF POTENTIAL ATTRITION ********************************************************************
. *** SOM 9: Assessing potential attrition bias
. tab _merge

   {txt}Matching result from {c |}
                  merge {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
        Master only (1) {c |}{res}        270       24.98       24.98
{txt}            Matched (3) {c |}{res}        811       75.02      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}      1,081      100.00
{txt}
{com}. *[Overall, we reinterviewed 75.02% of Wave 1 sample]
. 
. 
. *Generate dropout variable
. tab _merge, nolab

   {txt}Matching {c |}
result from {c |}
      merge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        270       24.98       24.98
{txt}          3 {c |}{res}        811       75.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. gen dropout = .
{txt}(1,081 missing values generated)

{com}. replace dropout = 1 if _merge == 1
{txt}(270 real changes made)

{com}. replace dropout = 0 if _merge == 3
{txt}(811 real changes made)

{com}. tab dropout 

    {txt}dropout {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        811       75.02       75.02
{txt}          1 {c |}{res}        270       24.98      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. 
. *** ATTRITION TESTS
. *Lagged Comp_scale_1 (measured at Wave 1) alone
. logit dropout Comp_scale_1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-591.90357}  
Iteration 1:{space 2}Log likelihood = {res:-583.42704}  
Iteration 2:{space 2}Log likelihood = {res:-583.26807}  
Iteration 3:{space 2}Log likelihood = {res:-583.26807}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,057}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:17.27}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-583.26807}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0146}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dropout{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Comp_scale_1 {c |}{col 14}{res}{space 2}-1.902788{col 26}{space 2} .4557757{col 37}{space 1}   -4.17{col 46}{space 3}0.000{col 54}{space 4}-2.796092{col 67}{space 3}-1.009484
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5738623{col 26}{space 2} .4067128{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.2232802{col 67}{space 3} 1.371005
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *Lagged Comp_scale_1 together with controls (all measured at Wave 1)
. logit dropout Comp_scale_1 c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 c.w1_victim_self i.sex c.age i.edu

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-526.79547}  
Iteration 1:{space 2}Log likelihood = {res:-495.44566}  
Iteration 2:{space 2}Log likelihood = {res:-494.63897}  
Iteration 3:{space 2}Log likelihood = {res:-494.63861}  
Iteration 4:{space 2}Log likelihood = {res:-494.63861}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:962}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:64.31}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-494.63861}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0610}

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             dropout{col 38}{c |} Coefficient{col 50}  Std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}Comp_scale_1 {c |}{col 38}{res}{space 2}-1.141371{col 50}{space 2} .5375453{col 61}{space 1}   -2.12{col 70}{space 3}0.034{col 78}{space 4}-2.194941{col 91}{space 3}-.0878021
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .1528675{col 50}{space 2} .4278174{col 61}{space 1}    0.36{col 70}{space 3}0.721{col 78}{space 4}-.6856392{col 91}{space 3} .9913742
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.7483885{col 50}{space 2} .4069631{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4}-1.546021{col 91}{space 3} .0492444
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2}-.1882361{col 50}{space 2} .4891973{col 61}{space 1}   -0.38{col 70}{space 3}0.700{col 78}{space 4}-1.147045{col 91}{space 3} .7705729
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .7100792{col 50}{space 2} .4367422{col 61}{space 1}    1.63{col 70}{space 3}0.104{col 78}{space 4}-.1459198{col 91}{space 3} 1.566078
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} -.262607{col 50}{space 2} .5558235{col 61}{space 1}   -0.47{col 70}{space 3}0.637{col 78}{space 4}-1.352001{col 91}{space 3}  .826787
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.4031576{col 50}{space 2} .3053884{col 61}{space 1}   -1.32{col 70}{space 3}0.187{col 78}{space 4}-1.001708{col 91}{space 3} .1953927
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} 1.266145{col 50}{space 2} .3635673{col 61}{space 1}    3.48{col 70}{space 3}0.000{col 78}{space 4} .5535657{col 91}{space 3} 1.978723
{txt}{space 22}w1_victim_self {c |}{col 38}{res}{space 2} .1160616{col 50}{space 2} .0752109{col 61}{space 1}    1.54{col 70}{space 3}0.123{col 78}{space 4} -.031349{col 91}{space 3} .2634722
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.2925704{col 50}{space 2} .1768579{col 61}{space 1}   -1.65{col 70}{space 3}0.098{col 78}{space 4}-.6392056{col 91}{space 3} .0540647
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0288053{col 50}{space 2}  .009446{col 61}{space 1}   -3.05{col 70}{space 3}0.002{col 78}{space 4}-.0473191{col 91}{space 3}-.0102916
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.2299669{col 50}{space 2} .3183674{col 61}{space 1}   -0.72{col 70}{space 3}0.470{col 78}{space 4}-.8539556{col 91}{space 3} .3940218
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.3861523{col 50}{space 2}  .389221{col 61}{space 1}   -0.99{col 70}{space 3}0.321{col 78}{space 4}-1.149011{col 91}{space 3} .3767069
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.1371803{col 50}{space 2} .3162278{col 61}{space 1}   -0.43{col 70}{space 3}0.664{col 78}{space 4}-.7569754{col 91}{space 3} .4826149
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.1555782{col 50}{space 2} .2870328{col 61}{space 1}   -0.54{col 70}{space 3}0.588{col 78}{space 4}-.7181521{col 91}{space 3} .4069958
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  1.39074{col 50}{space 2} .7394895{col 61}{space 1}    1.88{col 70}{space 3}0.060{col 78}{space 4}-.0586327{col 91}{space 3} 2.840113
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Lagged Warm_scale_1 (measured at Wave 1) alone
. logit dropout Warm_scale_1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-589.10812}  
Iteration 1:{space 2}Log likelihood = {res:-588.83995}  
Iteration 2:{space 2}Log likelihood = {res:-588.83989}  
Iteration 3:{space 2}Log likelihood = {res:-588.83989}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,055}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:0.54}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.4639}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-588.83989}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0005}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dropout{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Warm_scale_1 {c |}{col 14}{res}{space 2}-.2299565{col 26}{space 2} .3130999{col 37}{space 1}   -0.73{col 46}{space 3}0.463{col 54}{space 4}-.8436211{col 67}{space 3} .3837082
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9531062{col 26}{space 2} .2342877{col 37}{space 1}   -4.07{col 46}{space 3}0.000{col 54}{space 4}-1.412302{col 67}{space 3}-.4939108
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *Lagged Warm_scale_1 together with controls (all measured at Wave 1)
. logit dropout Warm_scale_1  c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 c.w1_victim_self i.sex c.age i.edu

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-526.79547}  
Iteration 1:{space 2}Log likelihood = {res:-497.64132}  
Iteration 2:{space 2}Log likelihood = {res:-496.93375}  
Iteration 3:{space 2}Log likelihood = {res:-496.93346}  
Iteration 4:{space 2}Log likelihood = {res:-496.93346}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:962}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:59.72}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-496.93346}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0567}

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             dropout{col 38}{c |} Coefficient{col 50}  Std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}Warm_scale_1 {c |}{col 38}{res}{space 2}-.0960439{col 50}{space 2} .3627443{col 61}{space 1}   -0.26{col 70}{space 3}0.791{col 78}{space 4}-.8070098{col 91}{space 3} .6149219
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .2124884{col 50}{space 2} .4270631{col 61}{space 1}    0.50{col 70}{space 3}0.619{col 78}{space 4}  -.62454{col 91}{space 3} 1.049517
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.8242786{col 50}{space 2}  .403782{col 61}{space 1}   -2.04{col 70}{space 3}0.041{col 78}{space 4}-1.615677{col 91}{space 3}-.0328803
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2}-.2522384{col 50}{space 2} .4867595{col 61}{space 1}   -0.52{col 70}{space 3}0.604{col 78}{space 4}-1.206269{col 91}{space 3} .7017927
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .6110285{col 50}{space 2} .4331884{col 61}{space 1}    1.41{col 70}{space 3}0.158{col 78}{space 4}-.2380052{col 91}{space 3} 1.460062
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} -.393159{col 50}{space 2} .5455725{col 61}{space 1}   -0.72{col 70}{space 3}0.471{col 78}{space 4}-1.462462{col 91}{space 3} .6761435
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} -.459691{col 50}{space 2} .3053844{col 61}{space 1}   -1.51{col 70}{space 3}0.132{col 78}{space 4}-1.058233{col 91}{space 3} .1388514
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} 1.236036{col 50}{space 2} .3621214{col 61}{space 1}    3.41{col 70}{space 3}0.001{col 78}{space 4} .5262912{col 91}{space 3} 1.945781
{txt}{space 22}w1_victim_self {c |}{col 38}{res}{space 2} .1382234{col 50}{space 2} .0744625{col 61}{space 1}    1.86{col 70}{space 3}0.063{col 78}{space 4}-.0077204{col 91}{space 3} .2841672
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.3324541{col 50}{space 2} .1758482{col 61}{space 1}   -1.89{col 70}{space 3}0.059{col 78}{space 4}-.6771102{col 91}{space 3} .0122021
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0293568{col 50}{space 2}  .009575{col 61}{space 1}   -3.07{col 70}{space 3}0.002{col 78}{space 4}-.0481234{col 91}{space 3}-.0105902
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.2805353{col 50}{space 2} .3149069{col 61}{space 1}   -0.89{col 70}{space 3}0.373{col 78}{space 4}-.8977415{col 91}{space 3} .3366708
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.4496327{col 50}{space 2} .3855694{col 61}{space 1}   -1.17{col 70}{space 3}0.244{col 78}{space 4}-1.205335{col 91}{space 3} .3060694
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.2069804{col 50}{space 2} .3125174{col 61}{space 1}   -0.66{col 70}{space 3}0.508{col 78}{space 4}-.8195033{col 91}{space 3} .4055425
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.2341197{col 50}{space 2} .2824316{col 61}{space 1}   -0.83{col 70}{space 3}0.407{col 78}{space 4}-.7876754{col 91}{space 3} .3194359
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .7929506{col 50}{space 2} .6993965{col 61}{space 1}    1.13{col 70}{space 3}0.257{col 78}{space 4}-.5778414{col 91}{space 3} 2.163743
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Lagged Warm_scale_1 (measured at Wave 1) alone
. logit dropout Domi_scale_1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-586.85865}  
Iteration 1:{space 2}Log likelihood = {res:-586.69847}  
Iteration 2:{space 2}Log likelihood = {res:-586.69846}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,051}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:0.32}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.5714}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-586.69846}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0003}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dropout{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Domi_scale_1 {c |}{col 14}{res}{space 2} .1566942{col 26}{space 2} .2769298{col 37}{space 1}    0.57{col 46}{space 3}0.572{col 54}{space 4}-.3860784{col 67}{space 3} .6994667
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.198385{col 26}{space 2} .1601652{col 37}{space 1}   -7.48{col 46}{space 3}0.000{col 54}{space 4}-1.512303{col 67}{space 3}-.8844674
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *Lagged Domi_scale_1 together with controls (all measured at Wave 1)
. logit dropout Domi_scale_1  c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 c.w1_victim_self i.sex c.age i.edu

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-526.79547}  
Iteration 1:{space 2}Log likelihood = {res:-496.76019}  
Iteration 2:{space 2}Log likelihood = {res: -496.0155}  
Iteration 3:{space 2}Log likelihood = {res:-496.01512}  
Iteration 4:{space 2}Log likelihood = {res:-496.01512}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:962}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:61.56}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-496.01512}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0584}

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             dropout{col 38}{c |} Coefficient{col 50}  Std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}Domi_scale_1 {c |}{col 38}{res}{space 2} .4529705{col 50}{space 2} .3140765{col 61}{space 1}    1.44{col 70}{space 3}0.149{col 78}{space 4}-.1626081{col 91}{space 3} 1.068549
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .1761816{col 50}{space 2} .4262723{col 61}{space 1}    0.41{col 70}{space 3}0.679{col 78}{space 4}-.6592967{col 91}{space 3}  1.01166
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.8707035{col 50}{space 2}  .403767{col 61}{space 1}   -2.16{col 70}{space 3}0.031{col 78}{space 4}-1.662072{col 91}{space 3}-.0793346
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2}-.2732188{col 50}{space 2} .4868099{col 61}{space 1}   -0.56{col 70}{space 3}0.575{col 78}{space 4}-1.227349{col 91}{space 3} .6809111
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .5542374{col 50}{space 2} .4342595{col 61}{space 1}    1.28{col 70}{space 3}0.202{col 78}{space 4}-.2968956{col 91}{space 3}  1.40537
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2}-.4211515{col 50}{space 2} .5453761{col 61}{space 1}   -0.77{col 70}{space 3}0.440{col 78}{space 4}-1.490069{col 91}{space 3} .6477661
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.4533843{col 50}{space 2} .3042895{col 61}{space 1}   -1.49{col 70}{space 3}0.136{col 78}{space 4}-1.049781{col 91}{space 3} .1430122
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} 1.187188{col 50}{space 2} .3623684{col 61}{space 1}    3.28{col 70}{space 3}0.001{col 78}{space 4} .4769591{col 91}{space 3} 1.897417
{txt}{space 22}w1_victim_self {c |}{col 38}{res}{space 2}  .133141{col 50}{space 2} .0745468{col 61}{space 1}    1.79{col 70}{space 3}0.074{col 78}{space 4}-.0129681{col 91}{space 3} .2792501
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.2984131{col 50}{space 2} .1777495{col 61}{space 1}   -1.68{col 70}{space 3}0.093{col 78}{space 4}-.6467958{col 91}{space 3} .0499696
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0302317{col 50}{space 2} .0094769{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} -.048806{col 91}{space 3}-.0116573
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.2986024{col 50}{space 2} .3144013{col 61}{space 1}   -0.95{col 70}{space 3}0.342{col 78}{space 4}-.9148176{col 91}{space 3} .3176127
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.4363078{col 50}{space 2} .3852685{col 61}{space 1}   -1.13{col 70}{space 3}0.257{col 78}{space 4} -1.19142{col 91}{space 3} .3188045
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.2050183{col 50}{space 2} .3121747{col 61}{space 1}   -0.66{col 70}{space 3}0.511{col 78}{space 4}-.8168695{col 91}{space 3} .4068329
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.2452661{col 50}{space 2} .2819104{col 61}{space 1}   -0.87{col 70}{space 3}0.384{col 78}{space 4}-.7978004{col 91}{space 3} .3072682
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6357274{col 50}{space 2} .6728737{col 61}{space 1}    0.94{col 70}{space 3}0.345{col 78}{space 4}-.6830807{col 91}{space 3} 1.954536
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *************************************************** SOM.10. ALTERNATIVE 'RALLY AROUND THE FLAG' EXPLANATION ********************************************************************
. *** SOM 10: Explores traits ratings of President Zelenskyy
. * Do average ratings of Zelensky closely mirror stated trait preferences in ideal leader?
. summ Comp_scale_Zel1 Warm_scale_Zel1 Domi_scale_Zel1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scal~l1 {c |}{res}      1,063    .7637713    .2908337          0          1
{txt}Warm_scal~l1 {c |}{res}      1,054    .7145003    .2931831          0          1
{txt}Domi_scal~l1 {c |}{res}      1,052    .4771863    .2808222          0          1
{txt}
{com}. 
. reg Comp_scale_Zel1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       531
{txt}{hline 13}{c +}{hline 34}   F(0, 530)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 46.2469941       530  .087258479   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 46.2469941       530  .087258479   {txt}Root MSE        =   {res}  .2954

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scal~l1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2}  .769094{col 26}{space 2} .0128191{col 37}{space 1}   60.00{col 46}{space 3}0.000{col 54}{space 4} .7439115{col 67}{space 3} .7942764
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_Zel1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       526
{txt}{hline 13}{c +}{hline 34}   F(0, 525)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 47.2798888       525  .090056931   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 47.2798888       525  .090056931   {txt}Root MSE        =   {res} .30009

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scal~l1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7148289{col 26}{space 2} .0130848{col 37}{space 1}   54.63{col 46}{space 3}0.000{col 54}{space 4}  .689124{col 67}{space 3} .7405338
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_Zel1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       526
{txt}{hline 13}{c +}{hline 34}   F(0, 525)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 45.1691083       525  .086036397   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 45.1691083       525  .086036397   {txt}Root MSE        =   {res} .29332

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scal~l1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4786122{col 26}{space 2} .0127893{col 37}{space 1}   37.42{col 46}{space 3}0.000{col 54}{space 4} .4534876{col 67}{space 3} .5037367
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. // Ranking of traits is similar as for ideal leader. But rating of Z's competence is 0.15 scale points lower than for ideal leader.
. 
. * Does context (war vs. peace) affect ratings of Zalenskyy
. reg Comp_scale_Zel1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,063
{txt}{hline 13}{c +}{hline 34}   F(1, 1061)      = {res}     0.36
{txt}       Model {c |} {res} .030058886         1  .030058886   {txt}Prob > F        ={res}    0.5513
{txt}    Residual {c |} {res} 89.7984354     1,061   .08463566   {txt}R-squared       ={res}    0.0003
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0006
{txt}       Total {c |} {res} 89.8284943     1,062   .08458427   {txt}Root MSE        =   {res} .29092

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_Z~1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0106353{col 28}{space 2}  .017846{col 39}{space 1}   -0.60{col 48}{space 3}0.551{col 56}{space 4}-.0456527{col 69}{space 3} .0243821
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  .769094{col 28}{space 2} .0126249{col 39}{space 1}   60.92{col 48}{space 3}0.000{col 56}{space 4} .7443213{col 69}{space 3} .7938666
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_Zel1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,054
{txt}{hline 13}{c +}{hline 34}   F(1, 1052)      = {res}     0.00
{txt}       Model {c |} {res} .000113364         1  .000113364   {txt}Prob > F        ={res}    0.9711
{txt}    Residual {c |} {res}  90.511882     1,052  .086037911   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0009
{txt}       Total {c |} {res} 90.5119954     1,053  .085956311   {txt}Root MSE        =   {res} .29332

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_Z~1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0006559{col 28}{space 2} .0180699{col 39}{space 1}   -0.04{col 48}{space 3}0.971{col 56}{space 4} -.036113{col 69}{space 3} .0348012
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .7148289{col 28}{space 2} .0127895{col 39}{space 1}   55.89{col 48}{space 3}0.000{col 56}{space 4} .6897332{col 69}{space 3} .7399246
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_Zel1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,052
{txt}{hline 13}{c +}{hline 34}   F(1, 1050)      = {res}     0.03
{txt}       Model {c |} {res}  .00213878         1   .00213878   {txt}Prob > F        ={res}    0.8693
{txt}    Residual {c |} {res} 82.8808866     1,050  .078934178   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0009
{txt}       Total {c |} {res} 82.8830254     1,051  .078861109   {txt}Root MSE        =   {res} .28095

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_Z~1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0028517{col 28}{space 2} .0173243{col 39}{space 1}   -0.16{col 48}{space 3}0.869{col 56}{space 4}-.0368458{col 69}{space 3} .0311424
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .4786122{col 28}{space 2} .0122501{col 39}{space 1}   39.07{col 48}{space 3}0.000{col 56}{space 4} .4545747{col 69}{space 3} .5026496
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. // Assigned context does not affect ratings of Zelensky
. 
. * Do Ratings of Zelensky correlate with stated trait preferences in ideal leader? NOT USED IN SOM.6
. pwcorr Comp_scale_1 Comp_scale_Zel1, sig // r=0.113

             {txt}{c |} Com~le_1 Comp_~l1
{hline 13}{c +}{hline 18}
Comp_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Comp_scal~l1 {c |} {res}  0.1130   1.0000 
             {txt}{c |}{res}   0.0002
             {txt}{c |}

{com}. pwcorr Warm_scale_1 Warm_scale_Zel1, sig // r=0.398

             {txt}{c |} Warm~e_1 Warm_~l1
{hline 13}{c +}{hline 18}
Warm_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Warm_scal~l1 {c |} {res}  0.3984   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Domi_scale_1 Domi_scale_Zel1, sig // r=0.443

             {txt}{c |} Domi~e_1 Domi_~l1
{hline 13}{c +}{hline 18}
Domi_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Domi_scal~l1 {c |} {res}  0.4431   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. // Ratings of Z and ideal leader correlate, but not overwhelmingly so
. 
. 
. *************************************************** SOM.11. INDIVIDUAL VICTIMIZATION OF RUSSIAN ATTACKS ********************************************************************
. 
. *** SOM.11: Exploring if self-reported victimization by Russian attacks affect leader trait preferences
. * Models controlling for changes in identification variables (models 1-3 in SOM.11)
. reg Comp_scale_diff c.Victimization_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       711
{txt}{hline 13}{c +}{hline 34}   F(4, 706)       = {res}     4.21
{txt}       Model {c |} {res} .283610561         4   .07090264   {txt}Prob > F        ={res}    0.0022
{txt}    Residual {c |} {res} 11.8871864       706  .016837375   {txt}R-squared       ={res}    0.0233
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0178
{txt}       Total {c |} {res}  12.170797       710  .017141968   {txt}Root MSE        =   {res} .12976

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Comp_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0475526{col 32}{space 2} .0189375{col 43}{space 1}   -2.51{col 52}{space 3}0.012{col 60}{space 4}-.0847331{col 73}{space 3} -.010372
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .1021717{col 32}{space 2} .0435817{col 43}{space 1}    2.34{col 52}{space 3}0.019{col 60}{space 4} .0166065{col 73}{space 3} .1877369
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .0231535{col 32}{space 2}   .02197{col 43}{space 1}    1.05{col 52}{space 3}0.292{col 60}{space 4}-.0199809{col 73}{space 3} .0662879
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0488814{col 32}{space 2} .0311958{col 43}{space 1}   -1.57{col 52}{space 3}0.118{col 60}{space 4}-.1101291{col 73}{space 3} .0123663
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .0003546{col 32}{space 2}  .005162{col 43}{space 1}    0.07{col 52}{space 3}0.945{col 60}{space 4}-.0097801{col 73}{space 3} .0104893
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_diff c.Victimization_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       711
{txt}{hline 13}{c +}{hline 34}   F(4, 706)       = {res}     4.63
{txt}       Model {c |} {res} .876387947         4  .219096987   {txt}Prob > F        ={res}    0.0011
{txt}    Residual {c |} {res} 33.4391868       706  .047364287   {txt}R-squared       ={res}    0.0255
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0200
{txt}       Total {c |} {res} 34.3155747       710  .048331795   {txt}Root MSE        =   {res} .21763

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Warm_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2} -.032306{col 32}{space 2} .0317622{col 43}{space 1}   -1.02{col 52}{space 3}0.309{col 60}{space 4}-.0946657{col 73}{space 3} .0300537
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0335988{col 32}{space 2} .0730958{col 43}{space 1}    0.46{col 52}{space 3}0.646{col 60}{space 4}-.1099123{col 73}{space 3} .1771099
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .1496797{col 32}{space 2} .0368484{col 43}{space 1}    4.06{col 52}{space 3}0.000{col 60}{space 4} .0773341{col 73}{space 3} .2220253
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0635483{col 32}{space 2}  .052322{col 43}{space 1}   -1.21{col 52}{space 3}0.225{col 60}{space 4}-.1662737{col 73}{space 3} .0391771
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-.0451014{col 32}{space 2} .0086578{col 43}{space 1}   -5.21{col 52}{space 3}0.000{col 60}{space 4}-.0620995{col 73}{space 3}-.0281034
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_diff c.Victimization_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       711
{txt}{hline 13}{c +}{hline 34}   F(4, 706)       = {res}     0.54
{txt}       Model {c |} {res} .109844408         4  .027461102   {txt}Prob > F        ={res}    0.7092
{txt}    Residual {c |} {res} 36.1560557       706  .051212543   {txt}R-squared       ={res}    0.0030
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0026
{txt}       Total {c |} {res} 36.2659001       710  .051078733   {txt}Root MSE        =   {res}  .2263

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Domi_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0260402{col 32}{space 2} .0330273{col 43}{space 1}   -0.79{col 52}{space 3}0.431{col 60}{space 4}-.0908838{col 73}{space 3} .0388033
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0590337{col 32}{space 2} .0760072{col 43}{space 1}    0.78{col 52}{space 3}0.438{col 60}{space 4}-.0901936{col 73}{space 3} .2082609
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2}-.0209786{col 32}{space 2} .0383161{col 43}{space 1}   -0.55{col 52}{space 3}0.584{col 60}{space 4}-.0962058{col 73}{space 3} .0542486
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2} .0524634{col 32}{space 2} .0544061{col 43}{space 1}    0.96{col 52}{space 3}0.335{col 60}{space 4}-.0543537{col 73}{space 3} .1592804
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .0229764{col 32}{space 2} .0090026{col 43}{space 1}    2.55{col 52}{space 3}0.011{col 60}{space 4} .0053013{col 73}{space 3} .0406514
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Models also controlling for changes in emotional reactions (models 4-6 in SOM.11)
. reg Comp_scale_diff c.Victimization_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       706
{txt}{hline 13}{c +}{hline 34}   F(8, 697)       = {res}     4.24
{txt}       Model {c |} {res} .563055925         8  .070381991   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 11.5636544       697  .016590609   {txt}R-squared       ={res}    0.0464
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0355
{txt}       Total {c |} {res} 12.1267104       705  .017201008   {txt}Root MSE        =   {res}  .1288

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Comp_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0506781{col 32}{space 2} .0189094{col 43}{space 1}   -2.68{col 52}{space 3}0.008{col 60}{space 4}-.0878044{col 73}{space 3}-.0135519
{txt}{space 5}fearfull_diff {c |}{col 20}{res}{space 2}-.0563143{col 32}{space 2} .0266272{col 43}{space 1}   -2.11{col 52}{space 3}0.035{col 60}{space 4}-.1085934{col 73}{space 3}-.0040352
{txt}{space 3}aggressive_diff {c |}{col 20}{res}{space 2} .0679219{col 32}{space 2} .0261436{col 43}{space 1}    2.60{col 52}{space 3}0.010{col 60}{space 4} .0165922{col 73}{space 3} .1192516
{txt}{space 6}sadness_diff {c |}{col 20}{res}{space 2} .0437024{col 32}{space 2} .0271144{col 43}{space 1}    1.61{col 52}{space 3}0.107{col 60}{space 4}-.0095332{col 73}{space 3} .0969381
{txt}{space 5}selfconf_diff {c |}{col 20}{res}{space 2} .0396815{col 32}{space 2} .0277011{col 43}{space 1}    1.43{col 52}{space 3}0.152{col 60}{space 4}-.0147061{col 73}{space 3} .0940691
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2}  .086504{col 32}{space 2} .0438741{col 43}{space 1}    1.97{col 52}{space 3}0.049{col 60}{space 4} .0003627{col 73}{space 3} .1726453
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .0156595{col 32}{space 2} .0219826{col 43}{space 1}    0.71{col 52}{space 3}0.476{col 60}{space 4}-.0275005{col 73}{space 3} .0588196
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0424028{col 32}{space 2} .0312252{col 43}{space 1}   -1.36{col 52}{space 3}0.175{col 60}{space 4}-.1037095{col 73}{space 3}  .018904
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-.0035746{col 32}{space 2} .0053924{col 43}{space 1}   -0.66{col 52}{space 3}0.508{col 60}{space 4} -.014162{col 73}{space 3} .0070127
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_diff c.Victimization_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       706
{txt}{hline 13}{c +}{hline 34}   F(8, 697)       = {res}     3.83
{txt}       Model {c |} {res} 1.44162484         8  .180203105   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 32.8261573       697  .047096352   {txt}R-squared       ={res}    0.0421
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0311
{txt}       Total {c |} {res} 34.2677822       705  .048606783   {txt}Root MSE        =   {res} .21702

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Warm_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0334488{col 32}{space 2} .0318596{col 43}{space 1}   -1.05{col 52}{space 3}0.294{col 60}{space 4}-.0960011{col 73}{space 3} .0291036
{txt}{space 5}fearfull_diff {c |}{col 20}{res}{space 2} -.077192{col 32}{space 2} .0448629{col 43}{space 1}   -1.72{col 52}{space 3}0.086{col 60}{space 4}-.1652747{col 73}{space 3} .0108907
{txt}{space 3}aggressive_diff {c |}{col 20}{res}{space 2} .0904302{col 32}{space 2} .0440482{col 43}{space 1}    2.05{col 52}{space 3}0.040{col 60}{space 4} .0039471{col 73}{space 3} .1769133
{txt}{space 6}sadness_diff {c |}{col 20}{res}{space 2} .1093213{col 32}{space 2} .0456838{col 43}{space 1}    2.39{col 52}{space 3}0.017{col 60}{space 4}  .019627{col 73}{space 3} .1990156
{txt}{space 5}selfconf_diff {c |}{col 20}{res}{space 2} -.048069{col 32}{space 2} .0466723{col 43}{space 1}   -1.03{col 52}{space 3}0.303{col 60}{space 4}-.1397042{col 73}{space 3} .0435661
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0310155{col 32}{space 2} .0739215{col 43}{space 1}    0.42{col 52}{space 3}0.675{col 60}{space 4}-.1141201{col 73}{space 3} .1761511
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .1389846{col 32}{space 2} .0370375{col 43}{space 1}    3.75{col 52}{space 3}0.000{col 60}{space 4} .0662662{col 73}{space 3} .2117029
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0566594{col 32}{space 2} .0526099{col 43}{space 1}   -1.08{col 52}{space 3}0.282{col 60}{space 4}-.1599523{col 73}{space 3} .0466336
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} -.050552{col 32}{space 2} .0090855{col 43}{space 1}   -5.56{col 52}{space 3}0.000{col 60}{space 4}-.0683902{col 73}{space 3}-.0327138
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_diff c.Victimization_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       706
{txt}{hline 13}{c +}{hline 34}   F(8, 697)       = {res}     1.02
{txt}       Model {c |} {res} .410676465         8  .051334558   {txt}Prob > F        ={res}    0.4212
{txt}    Residual {c |} {res} 35.1710921       697  .050460677   {txt}R-squared       ={res}    0.0115
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0002
{txt}       Total {c |} {res} 35.5817685       705  .050470594   {txt}Root MSE        =   {res} .22463

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Domi_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0334112{col 32}{space 2} .0329779{col 43}{space 1}   -1.01{col 52}{space 3}0.311{col 60}{space 4}-.0981592{col 73}{space 3} .0313368
{txt}{space 5}fearfull_diff {c |}{col 20}{res}{space 2} .0123753{col 32}{space 2} .0464377{col 43}{space 1}    0.27{col 52}{space 3}0.790{col 60}{space 4}-.0787993{col 73}{space 3} .1035498
{txt}{space 3}aggressive_diff {c |}{col 20}{res}{space 2}  .097824{col 32}{space 2} .0455944{col 43}{space 1}    2.15{col 52}{space 3}0.032{col 60}{space 4} .0083052{col 73}{space 3} .1873428
{txt}{space 6}sadness_diff {c |}{col 20}{res}{space 2} .0277847{col 32}{space 2} .0472873{col 43}{space 1}    0.59{col 52}{space 3}0.557{col 60}{space 4}-.0650581{col 73}{space 3} .1206274
{txt}{space 5}selfconf_diff {c |}{col 20}{res}{space 2} .0072708{col 32}{space 2} .0483106{col 43}{space 1}    0.15{col 52}{space 3}0.880{col 60}{space 4}-.0875809{col 73}{space 3} .1021225
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0475118{col 32}{space 2} .0765163{col 43}{space 1}    0.62{col 52}{space 3}0.535{col 60}{space 4}-.1027182{col 73}{space 3} .1977419
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2}-.0275922{col 32}{space 2} .0383375{col 43}{space 1}   -0.72{col 52}{space 3}0.472{col 60}{space 4}-.1028631{col 73}{space 3} .0476786
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2} .0520365{col 32}{space 2} .0544566{col 43}{space 1}    0.96{col 52}{space 3}0.340{col 60}{space 4}-.0548822{col 73}{space 3} .1589552
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .0233883{col 32}{space 2} .0094044{col 43}{space 1}    2.49{col 52}{space 3}0.013{col 60}{space 4} .0049239{col 73}{space 3} .0418526
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. *************************************************** SOM.12. OBLAST-LEVEL ANALYSES OF RUSSIAN ATTACKS ********************************************************************
. 
. *** SOM.12: Exploring if oblast-level differences in attack intensity relates to trait preferences in leaders
. ** Adding VIINA events to the dataset
. * Wave 1: N of events per oblast
. tab w1_q5

             {txt}5.1 Oblast {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
              Vinnytsia {c |}{res}         25        2.31        2.31
{txt}                  Volyn {c |}{res}         10        0.93        3.24
{txt}         Dnipropetrovsk {c |}{res}        122       11.29       14.52
{txt}                Donetsk {c |}{res}         33        3.05       17.58
{txt}               Zhytomyr {c |}{res}         27        2.50       20.07
{txt}        Transcarpathian {c |}{res}         15        1.39       21.46
{txt}           Zaporizhzhia {c |}{res}         62        5.74       27.20
{txt}        Ivano-Frankivsk {c |}{res}         28        2.59       29.79
{txt}                   Kyiv {c |}{res}         37        3.42       33.21
{txt}                   Kyiv {c |}{res}        195       18.04       51.25
{txt}             Kirovohrad {c |}{res}         21        1.94       53.19
{txt}                Luhansk {c |}{res}         10        0.93       54.12
{txt}                   Lviv {c |}{res}         62        5.74       59.85
{txt}               Mykolaiv {c |}{res}         39        3.61       63.46
{txt}                  Odesa {c |}{res}         71        6.57       70.03
{txt}                Poltava {c |}{res}         40        3.70       73.73
{txt}                  Rivne {c |}{res}         21        1.94       75.67
{txt}                   Sumy {c |}{res}         25        2.31       77.98
{txt}               Ternopil {c |}{res}         18        1.67       79.65
{txt}                Kharkiv {c |}{res}         94        8.70       88.34
{txt}                Kherson {c |}{res}         21        1.94       90.29
{txt}            Khmelnytsky {c |}{res}         28        2.59       92.88
{txt}               Cherkasy {c |}{res}         37        3.42       96.30
{txt}             Chernivtsi {c |}{res}         17        1.57       97.87
{txt}              Chernihiv {c |}{res}         23        2.13      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}      1,081      100.00
{txt}
{com}. tab w1_q5, nolab

 {txt}5.1 Oblast {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}         25        2.31        2.31
{txt}          3 {c |}{res}         10        0.93        3.24
{txt}          4 {c |}{res}        122       11.29       14.52
{txt}          5 {c |}{res}         33        3.05       17.58
{txt}          6 {c |}{res}         27        2.50       20.07
{txt}          7 {c |}{res}         15        1.39       21.46
{txt}          8 {c |}{res}         62        5.74       27.20
{txt}          9 {c |}{res}         28        2.59       29.79
{txt}         10 {c |}{res}         37        3.42       33.21
{txt}         11 {c |}{res}        195       18.04       51.25
{txt}         12 {c |}{res}         21        1.94       53.19
{txt}         13 {c |}{res}         10        0.93       54.12
{txt}         14 {c |}{res}         62        5.74       59.85
{txt}         15 {c |}{res}         39        3.61       63.46
{txt}         16 {c |}{res}         71        6.57       70.03
{txt}         17 {c |}{res}         40        3.70       73.73
{txt}         18 {c |}{res}         21        1.94       75.67
{txt}         19 {c |}{res}         25        2.31       77.98
{txt}         20 {c |}{res}         18        1.67       79.65
{txt}         21 {c |}{res}         94        8.70       88.34
{txt}         22 {c |}{res}         21        1.94       90.29
{txt}         23 {c |}{res}         28        2.59       92.88
{txt}         24 {c |}{res}         37        3.42       96.30
{txt}         25 {c |}{res}         17        1.57       97.87
{txt}         26 {c |}{res}         23        2.13      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. 
. ** N of VIINA events (all types) in the 2-week period before Wave 1
. * 2 Vinnytsya 279
. * 3 Volyn       120
. * 4 Dnipropetrovsk 279
. * 5 Donetsk 2101
. * 6 Zhytomyr 454
. * 7 Transcarpathian 76
. * 8 Zaporizhzhia 1280
. * 9 Ivano-Frankivsk 51
. * 10 Kyiv 1995
. * 11 Kyiv city 3704
. * 12 Kirovohrad 40
. * 13 Luhansk 720
. * 14 Lviv 393
. * 15 Mykolayiv 805
. * 16 Odessa 695
. * 17 Poltava 126
. * 18 Rivne 90
. * 19 Sumy 1091
. * 20 Ternopil 57
. * 21 Kharkiv 2436
. * 22 Kherson 938
. * 23 Khmelnytsky 121
. * 24 Cherkasy 139
. * 25 Chernivtsi 29
. * 26 Chernihiv 817
. * Crimea 387 [no respondents reached in Crimea]
. * Sevastopol 21 [no respondents reached in Sevastapol]
. 
. * Enters VIINA events/observations for Wave 1 to dataset
. gen w1_VIINA_events = .
{txt}(1,081 missing values generated)

{com}. replace w1_VIINA_events = 279 if w1_q5 == 2
{txt}(25 real changes made)

{com}. replace w1_VIINA_events = 120 if w1_q5 == 3
{txt}(10 real changes made)

{com}. replace w1_VIINA_events = 279 if w1_q5 == 4
{txt}(122 real changes made)

{com}. replace w1_VIINA_events = 2101 if w1_q5 == 5
{txt}(33 real changes made)

{com}. replace w1_VIINA_events = 454 if w1_q5 == 6
{txt}(27 real changes made)

{com}. replace w1_VIINA_events = 76 if w1_q5 == 7
{txt}(15 real changes made)

{com}. replace w1_VIINA_events = 1280 if w1_q5 == 8
{txt}(62 real changes made)

{com}. replace w1_VIINA_events = 51 if w1_q5 == 9
{txt}(28 real changes made)

{com}. replace w1_VIINA_events = 1995 if w1_q5 == 10
{txt}(37 real changes made)

{com}. replace w1_VIINA_events = 3704 if w1_q5 == 11
{txt}(195 real changes made)

{com}. replace w1_VIINA_events = 40 if w1_q5 == 12
{txt}(21 real changes made)

{com}. replace w1_VIINA_events = 720 if w1_q5 == 13
{txt}(10 real changes made)

{com}. replace w1_VIINA_events = 393 if w1_q5 == 14
{txt}(62 real changes made)

{com}. replace w1_VIINA_events = 805 if w1_q5 == 15
{txt}(39 real changes made)

{com}. replace w1_VIINA_events = 695 if w1_q5 == 16
{txt}(71 real changes made)

{com}. replace w1_VIINA_events = 126 if w1_q5 == 17
{txt}(40 real changes made)

{com}. replace w1_VIINA_events = 90 if w1_q5 == 18
{txt}(21 real changes made)

{com}. replace w1_VIINA_events = 1091 if w1_q5 == 19
{txt}(25 real changes made)

{com}. replace w1_VIINA_events = 57 if w1_q5 == 20
{txt}(18 real changes made)

{com}. replace w1_VIINA_events = 2436 if w1_q5 == 21
{txt}(94 real changes made)

{com}. replace w1_VIINA_events = 938 if w1_q5 == 22
{txt}(21 real changes made)

{com}. replace w1_VIINA_events = 121 if w1_q5 == 23
{txt}(28 real changes made)

{com}. replace w1_VIINA_events = 139 if w1_q5 == 24
{txt}(37 real changes made)

{com}. replace w1_VIINA_events = 29 if w1_q5 == 25
{txt}(17 real changes made)

{com}. replace w1_VIINA_events = 817 if w1_q5 == 26
{txt}(23 real changes made)

{com}. 
. ** Normalizes VIINA variable for Wave 1
. summ w1_VIINA_events

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
w1_VIINA_e~s {c |}{res}      1,081    1319.797     1327.05         29       3704
{txt}
{com}. gen VIINA_W1_norm = (w1_VIINA_events - r(min)) / (r(max) - r(min)) 
{txt}
{com}. summ VIINA_W1_norm

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
VIINA_W1_n~m {c |}{res}      1,081    .3512374    .3611019          0          1
{txt}
{com}. 
. ** Creates log-transformed version of the normalized VIINA variable for Wave 1
. gen ln_VIINA_W1_norm = ln(VIINA_W1_norm)
{txt}(17 missing values generated)

{com}. 
. 
. 
. ** N of VIINA events (all types) in the 2-week period before Wave 2
. * 2 Vinnytsya 72
. * 3 Volyn       150
. * 4 Dnipropetrovsk 349
. * 5 Donetsk 2274
. * 6 Zhytomyr 180
. * 7 Transcarpathian 117
. * 8 Zaporizhzhia 763
. * 9 Ivano-Frankivsk 37
. * 10 Kyiv 1435
. * 11 Kyiv city 1995
. * 12 Kirovohrad 20
. * 13 Luhansk 840
. * 14 Lviv 429
. * 15 Mykolayiv 415
. * 16 Odessa 486
. * 17 Poltava 86
. * 18 Rivne 120
. * 19 Sumy 465
. * 20 Ternopil 27
. * 21 Kharkiv 1258
. * 22 Kherson 709
. * 23 Khmelnytsky 82
. * 24 Cherkasy 32
. * 25 Chernivtsi 43
. * 26 Chernihiv 680
. * Crimea 335 [no respondents reached in Crimea]
. * Sevastopol 41 [no respondents reached in Sevastapol]
. 
. * Enters VIINA events/observations for Wave 2 to dataset
. gen w2_VIINA_events = .
{txt}(1,081 missing values generated)

{com}. replace w2_VIINA_events = 72 if w2_q4 == 2
{txt}(22 real changes made)

{com}. replace w2_VIINA_events = 150 if w2_q4 == 3
{txt}(6 real changes made)

{com}. replace w2_VIINA_events = 349 if w2_q4 == 4
{txt}(95 real changes made)

{com}. replace w2_VIINA_events = 2274 if w2_q4 == 5
{txt}(19 real changes made)

{com}. replace w2_VIINA_events = 180 if w2_q4 == 6
{txt}(20 real changes made)

{com}. replace w2_VIINA_events = 117 if w2_q4 == 7
{txt}(10 real changes made)

{com}. replace w2_VIINA_events = 763 if w2_q4 == 8
{txt}(48 real changes made)

{com}. replace w2_VIINA_events = 37 if w2_q4 == 9
{txt}(32 real changes made)

{com}. replace w2_VIINA_events = 1435 if w2_q4 == 10
{txt}(31 real changes made)

{com}. replace w2_VIINA_events = 1995 if w2_q4 == 11
{txt}(110 real changes made)

{com}. replace w2_VIINA_events = 20 if w2_q4 == 12
{txt}(18 real changes made)

{com}. replace w2_VIINA_events = 840 if w2_q4 == 13
{txt}(5 real changes made)

{com}. replace w2_VIINA_events = 429 if w2_q4 == 14
{txt}(60 real changes made)

{com}. replace w2_VIINA_events = 415 if w2_q4 == 15
{txt}(22 real changes made)

{com}. replace w2_VIINA_events = 486 if w2_q4 == 16
{txt}(50 real changes made)

{com}. replace w2_VIINA_events = 86 if w2_q4 == 17
{txt}(42 real changes made)

{com}. replace w2_VIINA_events = 120 if w2_q4 == 18
{txt}(20 real changes made)

{com}. replace w2_VIINA_events = 465 if w2_q4 == 19
{txt}(21 real changes made)

{com}. replace w2_VIINA_events = 27 if w2_q4 == 20
{txt}(17 real changes made)

{com}. replace w2_VIINA_events = 1258 if w2_q4 == 21
{txt}(36 real changes made)

{com}. replace w2_VIINA_events = 709 if w2_q4 == 22
{txt}(18 real changes made)

{com}. replace w2_VIINA_events = 82 if w2_q4 == 23
{txt}(16 real changes made)

{com}. replace w2_VIINA_events = 32 if w2_q4 == 24
{txt}(32 real changes made)

{com}. replace w2_VIINA_events = 43 if w2_q4 == 25
{txt}(13 real changes made)

{com}. replace w2_VIINA_events = 680 if w2_q4 == 26
{txt}(20 real changes made)

{com}. 
. ** Normalizes VIINA variable for Wave 1
. summ w2_VIINA_events

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
w2_VIINA_e~s {c |}{res}        783    689.4879    696.1808         20       2274
{txt}
{com}. gen VIINA_W2_norm = (w2_VIINA_events - r(min)) / (r(max) - r(min)) 
{txt}(298 missing values generated)

{com}. summ VIINA_W2_norm

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
VIINA_W2_n~m {c |}{res}        783    .2970221    .3088646          0          1
{txt}
{com}. 
. ** Creates log-transformed version of the normalized VIINA variable for Wave 1
. gen ln_VIINA_W2_norm = ln(VIINA_W2_norm)
{txt}(316 missing values generated)

{com}. 
. 
. 
. 
. *** Predictions of leader trait preferences from VIINA events (standard errors clustered at oblast-level)
. ** Wave 1 (Table SOM.12a)
. reg Comp_scale_1 VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}     1,012
                                                {txt}F(14, 24)         =  {res}    59.33
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1296
                                                {txt}Root MSE          =    {res} .13651

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0096757{col 50}{space 2} .0081714{col 61}{space 1}   -1.18{col 70}{space 3}0.248{col 78}{space 4}-.0265407{col 91}{space 3} .0071893
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2}-.0425799{col 50}{space 2} .0282878{col 61}{space 1}   -1.51{col 70}{space 3}0.145{col 78}{space 4}-.1009631{col 91}{space 3} .0158032
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}  .079017{col 50}{space 2} .0246296{col 61}{space 1}    3.21{col 70}{space 3}0.004{col 78}{space 4}  .028184{col 91}{space 3} .1298499
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0551254{col 50}{space 2} .0269799{col 61}{space 1}    2.04{col 70}{space 3}0.052{col 78}{space 4}-.0005583{col 91}{space 3} .1108091
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .0697216{col 50}{space 2} .0291557{col 61}{space 1}    2.39{col 70}{space 3}0.025{col 78}{space 4} .0095472{col 91}{space 3}  .129896
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2}  .131721{col 50}{space 2} .0479649{col 61}{space 1}    2.75{col 70}{space 3}0.011{col 78}{space 4} .0327264{col 91}{space 3} .2307156
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} .0437404{col 50}{space 2} .0244811{col 61}{space 1}    1.79{col 70}{space 3}0.087{col 78}{space 4}-.0067862{col 91}{space 3}  .094267
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}-.0061298{col 50}{space 2} .0325483{col 61}{space 1}   -0.19{col 70}{space 3}0.852{col 78}{space 4}-.0733062{col 91}{space 3} .0610466
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0224803{col 50}{space 2} .0081674{col 61}{space 1}    2.75{col 70}{space 3}0.011{col 78}{space 4} .0056236{col 91}{space 3} .0393371
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0002181{col 50}{space 2} .0005233{col 61}{space 1}   -0.42{col 70}{space 3}0.681{col 78}{space 4}-.0012982{col 91}{space 3}  .000862
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}  .027332{col 50}{space 2} .0200714{col 61}{space 1}    1.36{col 70}{space 3}0.186{col 78}{space 4}-.0140933{col 91}{space 3} .0687572
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0418732{col 50}{space 2} .0203612{col 61}{space 1}    2.06{col 70}{space 3}0.051{col 78}{space 4}-.0001503{col 91}{space 3} .0838968
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0420831{col 50}{space 2} .0230371{col 61}{space 1}    1.83{col 70}{space 3}0.080{col 78}{space 4}-.0054631{col 91}{space 3} .0896292
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0562099{col 50}{space 2} .0158051{col 61}{space 1}    3.56{col 70}{space 3}0.002{col 78}{space 4} .0235898{col 91}{space 3}   .08883
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5962104{col 50}{space 2} .0614431{col 61}{space 1}    9.70{col 70}{space 3}0.000{col 78}{space 4}  .469398{col 91}{space 3} .7230228
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_1 VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}     1,010
                                                {txt}F(14, 24)         =  {res}    38.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1123
                                                {txt}Root MSE          =    {res} .21519

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0184231{col 50}{space 2} .0128877{col 61}{space 1}   -1.43{col 70}{space 3}0.166{col 78}{space 4} -.045022{col 91}{space 3} .0081758
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .0990457{col 50}{space 2} .0441623{col 61}{space 1}    2.24{col 70}{space 3}0.034{col 78}{space 4} .0078993{col 91}{space 3} .1901921
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.0043112{col 50}{space 2} .0432222{col 61}{space 1}   -0.10{col 70}{space 3}0.921{col 78}{space 4}-.0935176{col 91}{space 3} .0848951
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0991296{col 50}{space 2} .0651628{col 61}{space 1}    1.52{col 70}{space 3}0.141{col 78}{space 4}-.0353598{col 91}{space 3}  .233619
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .1052093{col 50}{space 2} .0368444{col 61}{space 1}    2.86{col 70}{space 3}0.009{col 78}{space 4} .0291663{col 91}{space 3} .1812523
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .1046643{col 50}{space 2} .0707795{col 61}{space 1}    1.48{col 70}{space 3}0.152{col 78}{space 4}-.0414174{col 91}{space 3} .2507461
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} .0646321{col 50}{space 2} .0319918{col 61}{space 1}    2.02{col 70}{space 3}0.055{col 78}{space 4}-.0013956{col 91}{space 3} .1306598
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}  .005405{col 50}{space 2}  .033667{col 61}{space 1}    0.16{col 70}{space 3}0.874{col 78}{space 4}-.0640803{col 91}{space 3} .0748904
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0171565{col 50}{space 2} .0152081{col 61}{space 1}    1.13{col 70}{space 3}0.270{col 78}{space 4}-.0142314{col 91}{space 3} .0485445
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0048571{col 50}{space 2}  .000626{col 61}{space 1}   -7.76{col 70}{space 3}0.000{col 78}{space 4}-.0061491{col 91}{space 3}-.0035651
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0027496{col 50}{space 2}  .025919{col 61}{space 1}   -0.11{col 70}{space 3}0.916{col 78}{space 4}-.0562438{col 91}{space 3} .0507445
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} -.018459{col 50}{space 2} .0332708{col 61}{space 1}   -0.55{col 70}{space 3}0.584{col 78}{space 4}-.0871266{col 91}{space 3} .0502087
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0574968{col 50}{space 2} .0311708{col 61}{space 1}   -1.84{col 70}{space 3}0.077{col 78}{space 4}-.1218302{col 91}{space 3} .0068367
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0532765{col 50}{space 2} .0254322{col 61}{space 1}   -2.09{col 70}{space 3}0.047{col 78}{space 4}-.1057659{col 91}{space 3} -.000787
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6220129{col 50}{space 2} .0607372{col 61}{space 1}   10.24{col 70}{space 3}0.000{col 78}{space 4} .4966573{col 91}{space 3} .7473684
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_1 VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}     1,009
                                                {txt}F(14, 24)         =  {res}    16.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0617
                                                {txt}Root MSE          =    {res} .25267

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0402314{col 50}{space 2} .0276414{col 61}{space 1}   -1.46{col 70}{space 3}0.158{col 78}{space 4}-.0972804{col 91}{space 3} .0168176
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .0562479{col 50}{space 2} .0502032{col 61}{space 1}    1.12{col 70}{space 3}0.274{col 78}{space 4}-.0473664{col 91}{space 3} .1598623
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2} .0678551{col 50}{space 2} .0365506{col 61}{space 1}    1.86{col 70}{space 3}0.076{col 78}{space 4}-.0075817{col 91}{space 3} .1432918
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0546184{col 50}{space 2} .0648623{col 61}{space 1}    0.84{col 70}{space 3}0.408{col 78}{space 4}-.0792509{col 91}{space 3} .1884876
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .1348081{col 50}{space 2} .0453993{col 61}{space 1}    2.97{col 70}{space 3}0.007{col 78}{space 4} .0411085{col 91}{space 3} .2285077
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .0077534{col 50}{space 2} .0442257{col 61}{space 1}    0.18{col 70}{space 3}0.862{col 78}{space 4} -.083524{col 91}{space 3} .0990309
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.0361663{col 50}{space 2} .0313045{col 61}{space 1}   -1.16{col 70}{space 3}0.259{col 78}{space 4}-.1007757{col 91}{space 3} .0284431
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} .0686272{col 50}{space 2} .0318387{col 61}{space 1}    2.16{col 70}{space 3}0.041{col 78}{space 4} .0029154{col 91}{space 3}  .134339
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0940792{col 50}{space 2} .0155864{col 61}{space 1}   -6.04{col 70}{space 3}0.000{col 78}{space 4} -.126248{col 91}{space 3}-.0619103
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0019488{col 50}{space 2} .0011702{col 61}{space 1}    1.67{col 70}{space 3}0.109{col 78}{space 4}-.0004663{col 91}{space 3} .0043639
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2} .0258005{col 50}{space 2}  .035342{col 61}{space 1}    0.73{col 70}{space 3}0.472{col 78}{space 4}-.0471418{col 91}{space 3} .0987428
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0220183{col 50}{space 2} .0339259{col 61}{space 1}   -0.65{col 70}{space 3}0.522{col 78}{space 4}-.0920379{col 91}{space 3} .0480013
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0373062{col 50}{space 2} .0333662{col 61}{space 1}    1.12{col 70}{space 3}0.275{col 78}{space 4}-.0315583{col 91}{space 3} .1061708
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0460874{col 50}{space 2} .0253667{col 61}{space 1}    1.82{col 70}{space 3}0.082{col 78}{space 4}-.0062669{col 91}{space 3} .0984418
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}   .30615{col 50}{space 2} .0759566{col 61}{space 1}    4.03{col 70}{space 3}0.000{col 78}{space 4} .1493832{col 91}{space 3} .4629167
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Analyses based on log-transformed VIINA-variable (not reported in manuscript or SOM.10)
. reg Comp_scale_1 ln_VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}       998
                                                {txt}F(14, 23)         =  {res}    83.79
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1291
                                                {txt}Root MSE          =    {res} .13706

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0018457{col 50}{space 2} .0031777{col 61}{space 1}   -0.58{col 70}{space 3}0.567{col 78}{space 4}-.0084192{col 91}{space 3} .0047278
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} -.045936{col 50}{space 2} .0285429{col 61}{space 1}   -1.61{col 70}{space 3}0.121{col 78}{space 4}-.1049815{col 91}{space 3} .0131095
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2} .0765568{col 50}{space 2} .0247587{col 61}{space 1}    3.09{col 70}{space 3}0.005{col 78}{space 4} .0253395{col 91}{space 3} .1277741
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0595509{col 50}{space 2} .0265987{col 61}{space 1}    2.24{col 70}{space 3}0.035{col 78}{space 4} .0045273{col 91}{space 3} .1145745
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .0671073{col 50}{space 2} .0290721{col 61}{space 1}    2.31{col 70}{space 3}0.030{col 78}{space 4} .0069671{col 91}{space 3} .1272474
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .1351711{col 50}{space 2}  .048285{col 61}{space 1}    2.80{col 70}{space 3}0.010{col 78}{space 4}  .035286{col 91}{space 3} .2350562
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} .0436148{col 50}{space 2} .0248253{col 61}{space 1}    1.76{col 70}{space 3}0.092{col 78}{space 4}-.0077402{col 91}{space 3} .0949698
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}-.0058767{col 50}{space 2} .0328068{col 61}{space 1}   -0.18{col 70}{space 3}0.859{col 78}{space 4}-.0737427{col 91}{space 3} .0619894
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0233848{col 50}{space 2} .0081736{col 61}{space 1}    2.86{col 70}{space 3}0.009{col 78}{space 4} .0064764{col 91}{space 3} .0402932
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0001856{col 50}{space 2}  .000527{col 61}{space 1}   -0.35{col 70}{space 3}0.728{col 78}{space 4}-.0012757{col 91}{space 3} .0009045
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2} .0266658{col 50}{space 2} .0205398{col 61}{space 1}    1.30{col 70}{space 3}0.207{col 78}{space 4} -.015824{col 91}{space 3} .0691557
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0414762{col 50}{space 2}  .020729{col 61}{space 1}    2.00{col 70}{space 3}0.057{col 78}{space 4}-.0014049{col 91}{space 3} .0843573
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0411325{col 50}{space 2} .0232588{col 61}{space 1}    1.77{col 70}{space 3}0.090{col 78}{space 4} -.006982{col 91}{space 3} .0892469
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0553801{col 50}{space 2} .0161017{col 61}{space 1}    3.44{col 70}{space 3}0.002{col 78}{space 4} .0220712{col 91}{space 3}  .088689
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5873941{col 50}{space 2} .0618648{col 61}{space 1}    9.49{col 70}{space 3}0.000{col 78}{space 4} .4594169{col 91}{space 3} .7153713
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_1 ln_VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}       996
                                                {txt}F(14, 23)         =  {res}    27.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1120
                                                {txt}Root MSE          =    {res} .21547

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0003591{col 50}{space 2} .0035266{col 61}{space 1}   -0.10{col 70}{space 3}0.920{col 78}{space 4}-.0076544{col 91}{space 3} .0069363
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .0945964{col 50}{space 2} .0443955{col 61}{space 1}    2.13{col 70}{space 3}0.044{col 78}{space 4} .0027573{col 91}{space 3} .1864355
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.0082757{col 50}{space 2} .0436312{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4}-.0985338{col 91}{space 3} .0819824
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .1043609{col 50}{space 2} .0645404{col 61}{space 1}    1.62{col 70}{space 3}0.120{col 78}{space 4} -.029151{col 91}{space 3} .2378728
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2}  .103574{col 50}{space 2} .0369416{col 61}{space 1}    2.80{col 70}{space 3}0.010{col 78}{space 4} .0271545{col 91}{space 3} .1799936
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .1014041{col 50}{space 2} .0722151{col 61}{space 1}    1.40{col 70}{space 3}0.174{col 78}{space 4}-.0479841{col 91}{space 3} .2507923
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}  .065931{col 50}{space 2} .0318286{col 61}{space 1}    2.07{col 70}{space 3}0.050{col 78}{space 4} .0000886{col 91}{space 3} .1317734
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}  .002571{col 50}{space 2} .0335688{col 61}{space 1}    0.08{col 70}{space 3}0.940{col 78}{space 4}-.0668714{col 91}{space 3} .0720134
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0200629{col 50}{space 2} .0148841{col 61}{space 1}    1.35{col 70}{space 3}0.191{col 78}{space 4}-.0107272{col 91}{space 3} .0508531
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0050409{col 50}{space 2} .0006165{col 61}{space 1}   -8.18{col 70}{space 3}0.000{col 78}{space 4}-.0063163{col 91}{space 3}-.0037655
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0061837{col 50}{space 2} .0263486{col 61}{space 1}   -0.23{col 70}{space 3}0.817{col 78}{space 4}-.0606899{col 91}{space 3} .0483225
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0234977{col 50}{space 2} .0338063{col 61}{space 1}   -0.70{col 70}{space 3}0.494{col 78}{space 4}-.0934313{col 91}{space 3} .0464359
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0605594{col 50}{space 2} .0311683{col 61}{space 1}   -1.94{col 70}{space 3}0.064{col 78}{space 4} -.125036{col 91}{space 3} .0039172
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0532127{col 50}{space 2} .0256987{col 61}{space 1}   -2.07{col 70}{space 3}0.050{col 78}{space 4}-.1063745{col 91}{space 3}-.0000509
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .626588{col 50}{space 2} .0623239{col 61}{space 1}   10.05{col 70}{space 3}0.000{col 78}{space 4} .4976613{col 91}{space 3} .7555148
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_1 ln_VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}       995
                                                {txt}F(14, 23)         =  {res}    30.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0676
                                                {txt}Root MSE          =    {res} .25171

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0125897{col 50}{space 2} .0067036{col 61}{space 1}   -1.88{col 70}{space 3}0.073{col 78}{space 4}-.0264572{col 91}{space 3} .0012777
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2}  .074113{col 50}{space 2} .0507521{col 61}{space 1}    1.46{col 70}{space 3}0.158{col 78}{space 4}-.0308758{col 91}{space 3} .1791017
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2} .0667022{col 50}{space 2}  .037226{col 61}{space 1}    1.79{col 70}{space 3}0.086{col 78}{space 4}-.0103056{col 91}{space 3} .1437101
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0342702{col 50}{space 2} .0646542{col 61}{space 1}    0.53{col 70}{space 3}0.601{col 78}{space 4}-.0994772{col 91}{space 3} .1680177
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2}  .136295{col 50}{space 2} .0450642{col 61}{space 1}    3.02{col 70}{space 3}0.006{col 78}{space 4} .0430727{col 91}{space 3} .2295173
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .0118174{col 50}{space 2} .0439589{col 61}{space 1}    0.27{col 70}{space 3}0.790{col 78}{space 4}-.0791185{col 91}{space 3} .1027533
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.0385554{col 50}{space 2} .0305963{col 61}{space 1}   -1.26{col 70}{space 3}0.220{col 78}{space 4}-.1018486{col 91}{space 3} .0247378
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} .0760227{col 50}{space 2} .0321126{col 61}{space 1}    2.37{col 70}{space 3}0.027{col 78}{space 4} .0095927{col 91}{space 3} .1424526
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0971882{col 50}{space 2} .0155146{col 61}{space 1}   -6.26{col 70}{space 3}0.000{col 78}{space 4}-.1292826{col 91}{space 3}-.0650939
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0021419{col 50}{space 2} .0011851{col 61}{space 1}    1.81{col 70}{space 3}0.084{col 78}{space 4}-.0003097{col 91}{space 3} .0045935
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}  .024921{col 50}{space 2} .0363144{col 61}{space 1}    0.69{col 70}{space 3}0.499{col 78}{space 4} -.050201{col 91}{space 3}  .100043
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0279341{col 50}{space 2} .0338698{col 61}{space 1}   -0.82{col 70}{space 3}0.418{col 78}{space 4}-.0979992{col 91}{space 3} .0421309
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}  .036013{col 50}{space 2} .0342361{col 61}{space 1}    1.05{col 70}{space 3}0.304{col 78}{space 4}-.0348097{col 91}{space 3} .1068357
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0431387{col 50}{space 2} .0259745{col 61}{space 1}    1.66{col 70}{space 3}0.110{col 78}{space 4}-.0105937{col 91}{space 3}  .096871
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2647037{col 50}{space 2} .0822248{col 61}{space 1}    3.22{col 70}{space 3}0.004{col 78}{space 4} .0946087{col 91}{space 3} .4347987
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Based on wave 2 data (Table SOM.12b)
. reg Comp_scale_2 VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       740
                                                {txt}F(14, 24)         =  {res}    42.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1241
                                                {txt}Root MSE          =    {res} .13133

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}VIINA_W2_norm {c |}{col 38}{res}{space 2} .0013251{col 50}{space 2} .0101465{col 61}{space 1}    0.13{col 70}{space 3}0.897{col 78}{space 4}-.0196163{col 91}{space 3} .0222665
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2}-.0855858{col 50}{space 2} .0172142{col 61}{space 1}   -4.97{col 70}{space 3}0.000{col 78}{space 4}-.1211142{col 91}{space 3}-.0500573
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0573742{col 50}{space 2} .0252004{col 61}{space 1}    2.28{col 70}{space 3}0.032{col 78}{space 4} .0053631{col 91}{space 3} .1093854
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2}  .086151{col 50}{space 2} .0257919{col 61}{space 1}    3.34{col 70}{space 3}0.003{col 78}{space 4} .0329192{col 91}{space 3} .1393828
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .0622659{col 50}{space 2} .0289909{col 61}{space 1}    2.15{col 70}{space 3}0.042{col 78}{space 4} .0024317{col 91}{space 3} .1221001
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .1593679{col 50}{space 2} .0377353{col 61}{space 1}    4.22{col 70}{space 3}0.000{col 78}{space 4}  .081486{col 91}{space 3} .2372497
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0496091{col 50}{space 2} .0259615{col 61}{space 1}    1.91{col 70}{space 3}0.068{col 78}{space 4}-.0039729{col 91}{space 3}  .103191
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2}-.0129365{col 50}{space 2} .0485734{col 61}{space 1}   -0.27{col 70}{space 3}0.792{col 78}{space 4} -.113187{col 91}{space 3} .0873141
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0257735{col 50}{space 2} .0077827{col 61}{space 1}    3.31{col 70}{space 3}0.003{col 78}{space 4} .0097108{col 91}{space 3} .0418362
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0005816{col 50}{space 2} .0005705{col 61}{space 1}   -1.02{col 70}{space 3}0.318{col 78}{space 4} -.001759{col 91}{space 3} .0005959
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0037926{col 50}{space 2} .0281257{col 61}{space 1}   -0.13{col 70}{space 3}0.894{col 78}{space 4}-.0618413{col 91}{space 3}  .054256
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0080863{col 50}{space 2} .0273433{col 61}{space 1}    0.30{col 70}{space 3}0.770{col 78}{space 4}-.0483475{col 91}{space 3} .0645202
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0166586{col 50}{space 2} .0255861{col 61}{space 1}    0.65{col 70}{space 3}0.521{col 78}{space 4}-.0361485{col 91}{space 3} .0694658
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0193869{col 50}{space 2} .0229983{col 61}{space 1}    0.84{col 70}{space 3}0.408{col 78}{space 4}-.0280792{col 91}{space 3}  .066853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6310441{col 50}{space 2} .0533802{col 61}{space 1}   11.82{col 70}{space 3}0.000{col 78}{space 4} .5208729{col 91}{space 3} .7412154
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_2 VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       728
                                                {txt}F(14, 24)         =  {res}    24.64
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1206
                                                {txt}Root MSE          =    {res} .23544

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}VIINA_W2_norm {c |}{col 38}{res}{space 2} .0191862{col 50}{space 2} .0202804{col 61}{space 1}    0.95{col 70}{space 3}0.354{col 78}{space 4}-.0226705{col 91}{space 3} .0610429
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .1061628{col 50}{space 2} .0456334{col 61}{space 1}    2.33{col 70}{space 3}0.029{col 78}{space 4} .0119801{col 91}{space 3} .2003454
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2}-.0685957{col 50}{space 2} .0511487{col 61}{space 1}   -1.34{col 70}{space 3}0.192{col 78}{space 4}-.1741615{col 91}{space 3}   .03697
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0501229{col 50}{space 2} .0580696{col 61}{space 1}    0.86{col 70}{space 3}0.397{col 78}{space 4} -.069727{col 91}{space 3} .1699727
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1474133{col 50}{space 2} .0433574{col 61}{space 1}    3.40{col 70}{space 3}0.002{col 78}{space 4} .0579279{col 91}{space 3} .2368987
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .2016106{col 50}{space 2} .0588632{col 61}{space 1}    3.43{col 70}{space 3}0.002{col 78}{space 4}  .080123{col 91}{space 3} .3230982
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0645799{col 50}{space 2} .0378985{col 61}{space 1}    1.70{col 70}{space 3}0.101{col 78}{space 4}-.0136388{col 91}{space 3} .1427986
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0568291{col 50}{space 2} .0465705{col 61}{space 1}    1.22{col 70}{space 3}0.234{col 78}{space 4}-.0392877{col 91}{space 3} .1529459
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}   .03858{col 50}{space 2} .0155066{col 61}{space 1}    2.49{col 70}{space 3}0.020{col 78}{space 4} .0065759{col 91}{space 3} .0705841
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0064922{col 50}{space 2} .0009544{col 61}{space 1}   -6.80{col 70}{space 3}0.000{col 78}{space 4} -.008462{col 91}{space 3}-.0045223
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0502897{col 50}{space 2}  .037747{col 61}{space 1}   -1.33{col 70}{space 3}0.195{col 78}{space 4}-.1281957{col 91}{space 3} .0276164
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0862869{col 50}{space 2} .0641405{col 61}{space 1}   -1.35{col 70}{space 3}0.191{col 78}{space 4}-.2186663{col 91}{space 3} .0460926
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0627934{col 50}{space 2} .0461403{col 61}{space 1}   -1.36{col 70}{space 3}0.186{col 78}{space 4}-.1580222{col 91}{space 3} .0324355
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0775398{col 50}{space 2}  .045346{col 61}{space 1}   -1.71{col 70}{space 3}0.100{col 78}{space 4}-.1711293{col 91}{space 3} .0160496
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5920556{col 50}{space 2} .0775404{col 61}{space 1}    7.64{col 70}{space 3}0.000{col 78}{space 4} .4320201{col 91}{space 3} .7520911
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_2 VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       728
                                                {txt}F(14, 24)         =  {res}    13.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0562
                                                {txt}Root MSE          =    {res} .25348

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}VIINA_W2_norm {c |}{col 38}{res}{space 2}-.0797794{col 50}{space 2} .0171241{col 61}{space 1}   -4.66{col 70}{space 3}0.000{col 78}{space 4}-.1151218{col 91}{space 3}-.0444369
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .0712021{col 50}{space 2} .0453258{col 61}{space 1}    1.57{col 70}{space 3}0.129{col 78}{space 4}-.0223458{col 91}{space 3}   .16475
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0620388{col 50}{space 2} .0657703{col 61}{space 1}    0.94{col 70}{space 3}0.355{col 78}{space 4}-.0737044{col 91}{space 3}  .197782
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0454363{col 50}{space 2} .0370624{col 61}{space 1}    1.23{col 70}{space 3}0.232{col 78}{space 4}-.0310567{col 91}{space 3} .1219293
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1046817{col 50}{space 2} .0510098{col 61}{space 1}    2.05{col 70}{space 3}0.051{col 78}{space 4}-.0005975{col 91}{space 3} .2099608
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2}-.0339921{col 50}{space 2} .0487065{col 61}{space 1}   -0.70{col 70}{space 3}0.492{col 78}{space 4}-.1345175{col 91}{space 3} .0665333
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0057272{col 50}{space 2} .0433362{col 61}{space 1}    0.13{col 70}{space 3}0.896{col 78}{space 4}-.0837143{col 91}{space 3} .0951687
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0355095{col 50}{space 2} .0786136{col 61}{space 1}    0.45{col 70}{space 3}0.656{col 78}{space 4} -.126741{col 91}{space 3}   .19776
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0913048{col 50}{space 2} .0183105{col 61}{space 1}   -4.99{col 70}{space 3}0.000{col 78}{space 4}-.1290958{col 91}{space 3}-.0535138
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0006676{col 50}{space 2} .0010249{col 61}{space 1}    0.65{col 70}{space 3}0.521{col 78}{space 4}-.0014477{col 91}{space 3} .0027828
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0383892{col 50}{space 2} .0405658{col 61}{space 1}   -0.95{col 70}{space 3}0.353{col 78}{space 4}-.1221128{col 91}{space 3} .0453344
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0746896{col 50}{space 2} .0531394{col 61}{space 1}   -1.41{col 70}{space 3}0.173{col 78}{space 4} -.184364{col 91}{space 3} .0349847
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0241032{col 50}{space 2}  .035892{col 61}{space 1}   -0.67{col 70}{space 3}0.508{col 78}{space 4}-.0981808{col 91}{space 3} .0499743
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0445354{col 50}{space 2} .0369568{col 61}{space 1}   -1.21{col 70}{space 3}0.240{col 78}{space 4}-.1208104{col 91}{space 3} .0317396
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4846569{col 50}{space 2} .0585157{col 61}{space 1}    8.28{col 70}{space 3}0.000{col 78}{space 4} .3638863{col 91}{space 3} .6054274
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Analyses based on log-transformed VIINA-variable (not reported in manuscript or SOM.10)
. reg Comp_scale_2 ln_VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       727
                                                {txt}F(14, 23)         =  {res}    32.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1339
                                                {txt}Root MSE          =    {res} .13041

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W2_norm {c |}{col 38}{res}{space 2} .0025742{col 50}{space 2} .0028938{col 61}{space 1}    0.89{col 70}{space 3}0.383{col 78}{space 4}-.0034122{col 91}{space 3} .0085605
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2}   -.0855{col 50}{space 2} .0180433{col 61}{space 1}   -4.74{col 70}{space 3}0.000{col 78}{space 4}-.1228254{col 91}{space 3}-.0481745
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0579847{col 50}{space 2} .0260042{col 61}{space 1}    2.23{col 70}{space 3}0.036{col 78}{space 4} .0041909{col 91}{space 3} .1117786
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0892843{col 50}{space 2} .0265635{col 61}{space 1}    3.36{col 70}{space 3}0.003{col 78}{space 4} .0343335{col 91}{space 3} .1442351
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .0692507{col 50}{space 2} .0285262{col 61}{space 1}    2.43{col 70}{space 3}0.023{col 78}{space 4} .0102396{col 91}{space 3} .1282617
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .1648015{col 50}{space 2} .0379748{col 61}{space 1}    4.34{col 70}{space 3}0.000{col 78}{space 4} .0862445{col 91}{space 3} .2433584
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0481153{col 50}{space 2} .0264057{col 61}{space 1}    1.82{col 70}{space 3}0.081{col 78}{space 4} -.006509{col 91}{space 3} .1027396
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2}-.0153309{col 50}{space 2} .0485989{col 61}{space 1}   -0.32{col 70}{space 3}0.755{col 78}{space 4}-.1158653{col 91}{space 3} .0852034
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0276918{col 50}{space 2}  .007823{col 61}{space 1}    3.54{col 70}{space 3}0.002{col 78}{space 4} .0115087{col 91}{space 3} .0438749
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0006752{col 50}{space 2} .0005732{col 61}{space 1}   -1.18{col 70}{space 3}0.251{col 78}{space 4} -.001861{col 91}{space 3} .0005106
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0040943{col 50}{space 2} .0285078{col 61}{space 1}   -0.14{col 70}{space 3}0.887{col 78}{space 4}-.0630672{col 91}{space 3} .0548786
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0084034{col 50}{space 2} .0272693{col 61}{space 1}    0.31{col 70}{space 3}0.761{col 78}{space 4}-.0480074{col 91}{space 3} .0648143
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0169186{col 50}{space 2} .0257265{col 61}{space 1}    0.66{col 70}{space 3}0.517{col 78}{space 4}-.0363007{col 91}{space 3}  .070138
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0195904{col 50}{space 2} .0232152{col 61}{space 1}    0.84{col 70}{space 3}0.407{col 78}{space 4} -.028434{col 91}{space 3} .0676148
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6283565{col 50}{space 2} .0525998{col 61}{space 1}   11.95{col 70}{space 3}0.000{col 78}{space 4} .5195454{col 91}{space 3} .7371675
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_2 ln_VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       715
                                                {txt}F(14, 23)         =  {res}    26.02
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1240
                                                {txt}Root MSE          =    {res} .23538

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W2_norm {c |}{col 38}{res}{space 2} .0029692{col 50}{space 2} .0060121{col 61}{space 1}    0.49{col 70}{space 3}0.626{col 78}{space 4}-.0094679{col 91}{space 3} .0154062
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .1032017{col 50}{space 2}  .044984{col 61}{space 1}    2.29{col 70}{space 3}0.031{col 78}{space 4} .0101451{col 91}{space 3} .1962582
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2}-.0807987{col 50}{space 2} .0504046{col 61}{space 1}   -1.60{col 70}{space 3}0.123{col 78}{space 4}-.1850684{col 91}{space 3} .0234711
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0592763{col 50}{space 2} .0566496{col 61}{space 1}    1.05{col 70}{space 3}0.306{col 78}{space 4}-.0579123{col 91}{space 3} .1764649
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1550581{col 50}{space 2} .0433985{col 61}{space 1}    3.57{col 70}{space 3}0.002{col 78}{space 4} .0652815{col 91}{space 3} .2448347
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .2068077{col 50}{space 2} .0590759{col 61}{space 1}    3.50{col 70}{space 3}0.002{col 78}{space 4} .0845998{col 91}{space 3} .3290156
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0634301{col 50}{space 2} .0383242{col 61}{space 1}    1.66{col 70}{space 3}0.111{col 78}{space 4}-.0158496{col 91}{space 3} .1427097
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0493542{col 50}{space 2} .0452149{col 61}{space 1}    1.09{col 70}{space 3}0.286{col 78}{space 4}-.0441801{col 91}{space 3} .1428884
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0408103{col 50}{space 2} .0156274{col 61}{space 1}    2.61{col 70}{space 3}0.016{col 78}{space 4} .0084827{col 91}{space 3}  .073138
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0064855{col 50}{space 2} .0009774{col 61}{space 1}   -6.64{col 70}{space 3}0.000{col 78}{space 4}-.0085073{col 91}{space 3}-.0044637
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0507233{col 50}{space 2} .0378916{col 61}{space 1}   -1.34{col 70}{space 3}0.194{col 78}{space 4} -.129108{col 91}{space 3} .0276615
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0861958{col 50}{space 2} .0636228{col 61}{space 1}   -1.35{col 70}{space 3}0.189{col 78}{space 4}-.2178096{col 91}{space 3}  .045418
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0589737{col 50}{space 2} .0466465{col 61}{space 1}   -1.26{col 70}{space 3}0.219{col 78}{space 4}-.1554695{col 91}{space 3}  .037522
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0791958{col 50}{space 2} .0451556{col 61}{space 1}   -1.75{col 70}{space 3}0.093{col 78}{space 4}-.1726073{col 91}{space 3} .0142158
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5983831{col 50}{space 2} .0800611{col 61}{space 1}    7.47{col 70}{space 3}0.000{col 78}{space 4} .4327642{col 91}{space 3} .7640021
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_2 ln_VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       715
                                                {txt}F(14, 23)         =  {res}    18.78
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0568
                                                {txt}Root MSE          =    {res} .25407

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W2_norm {c |}{col 38}{res}{space 2}-.0155218{col 50}{space 2} .0046615{col 61}{space 1}   -3.33{col 70}{space 3}0.003{col 78}{space 4}-.0251647{col 91}{space 3}-.0058788
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .0834996{col 50}{space 2} .0441809{col 61}{space 1}    1.89{col 70}{space 3}0.071{col 78}{space 4}-.0078956{col 91}{space 3} .1748948
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0548506{col 50}{space 2} .0663899{col 61}{space 1}    0.83{col 70}{space 3}0.417{col 78}{space 4}-.0824873{col 91}{space 3} .1921885
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2}   .02757{col 50}{space 2} .0374698{col 61}{space 1}    0.74{col 70}{space 3}0.469{col 78}{space 4}-.0499422{col 91}{space 3} .1050822
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1010124{col 50}{space 2} .0517046{col 61}{space 1}    1.95{col 70}{space 3}0.063{col 78}{space 4}-.0059467{col 91}{space 3} .2079715
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2}-.0267146{col 50}{space 2} .0502332{col 61}{space 1}   -0.53{col 70}{space 3}0.600{col 78}{space 4}  -.13063{col 91}{space 3} .0772008
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0081026{col 50}{space 2} .0434242{col 61}{space 1}    0.19{col 70}{space 3}0.854{col 78}{space 4}-.0817273{col 91}{space 3} .0979325
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0341445{col 50}{space 2} .0798484{col 61}{space 1}    0.43{col 70}{space 3}0.673{col 78}{space 4}-.1310345{col 91}{space 3} .1993235
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0956663{col 50}{space 2} .0182986{col 61}{space 1}   -5.23{col 70}{space 3}0.000{col 78}{space 4}-.1335197{col 91}{space 3}-.0578128
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0005498{col 50}{space 2} .0010328{col 61}{space 1}    0.53{col 70}{space 3}0.600{col 78}{space 4}-.0015866{col 91}{space 3} .0026863
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0399239{col 50}{space 2} .0412829{col 61}{space 1}   -0.97{col 70}{space 3}0.344{col 78}{space 4}-.1253241{col 91}{space 3} .0454762
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0734172{col 50}{space 2} .0539684{col 61}{space 1}   -1.36{col 70}{space 3}0.187{col 78}{space 4}-.1850594{col 91}{space 3}  .038225
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0244657{col 50}{space 2} .0360509{col 61}{space 1}   -0.68{col 70}{space 3}0.504{col 78}{space 4}-.0990428{col 91}{space 3} .0501113
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0412472{col 50}{space 2} .0374474{col 61}{space 1}   -1.10{col 70}{space 3}0.282{col 78}{space 4} -.118713{col 91}{space 3} .0362186
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4383067{col 50}{space 2} .0555308{col 61}{space 1}    7.89{col 70}{space 3}0.000{col 78}{space 4} .3234325{col 91}{space 3} .5531809
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. **************************************************************************************************************************************************
. ********************************************************** RESHAPES DATASET TO LONG FORMAT *******************************************************
. **************************************************************************************************************************************************
. reshape long Competence_ Trustworthy_ Dominant_ Generous_ Strong_ Warm_ Toughminded_ Comp_scale_ Warm_scale_ Domi_scale_ Comp_PCA_ Warm_PCA_ Domi_PCA_ ///
> afraid_ frightened_ scared_ angry_ hostile_ disgusted_ sad_ lonely_ downhearted_ proud_ strong_ confident_ anxiety_scale_ aggressive_scale_ sadness_scale_ selfconf_scale_ Victimization_ ///
> Conflict_ ID_Ukraine_ ID_Russia_ ID_Europe_, i(ID_random) j(wave)
{txt}(j = 1 2)
(variable {bf:anxiety_scale_1} not found)
(variable {bf:anxiety_scale_2} not found)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}       1,081   {txt}->   {res}2,162       
{txt}Number of variables        {res}         324   {txt}->   {res}293         
{txt}j variable (2 values)                     ->   {res}wave
{txt}xij variables:
              {res}Competence_1 Competence_2   {txt}->   {res}Competence_
            Trustworthy_1 Trustworthy_2   {txt}->   {res}Trustworthy_
                  Dominant_1 Dominant_2   {txt}->   {res}Dominant_
                  Generous_1 Generous_2   {txt}->   {res}Generous_
                      Strong_1 Strong_2   {txt}->   {res}Strong_
                          Warm_1 Warm_2   {txt}->   {res}Warm_
            Toughminded_1 Toughminded_2   {txt}->   {res}Toughminded_
              Comp_scale_1 Comp_scale_2   {txt}->   {res}Comp_scale_
              Warm_scale_1 Warm_scale_2   {txt}->   {res}Warm_scale_
              Domi_scale_1 Domi_scale_2   {txt}->   {res}Domi_scale_
                  Comp_PCA_1 Comp_PCA_2   {txt}->   {res}Comp_PCA_
                  Warm_PCA_1 Warm_PCA_2   {txt}->   {res}Warm_PCA_
                  Domi_PCA_1 Domi_PCA_2   {txt}->   {res}Domi_PCA_
                      afraid_1 afraid_2   {txt}->   {res}afraid_
              frightened_1 frightened_2   {txt}->   {res}frightened_
                      scared_1 scared_2   {txt}->   {res}scared_
                        angry_1 angry_2   {txt}->   {res}angry_
                    hostile_1 hostile_2   {txt}->   {res}hostile_
                disgusted_1 disgusted_2   {txt}->   {res}disgusted_
                            sad_1 sad_2   {txt}->   {res}sad_
                      lonely_1 lonely_2   {txt}->   {res}lonely_
            downhearted_1 downhearted_2   {txt}->   {res}downhearted_
                        proud_1 proud_2   {txt}->   {res}proud_
                      strong_1 strong_2   {txt}->   {res}strong_
                confident_1 confident_2   {txt}->   {res}confident_
        anxiety_scale_1 anxiety_scale_2   {txt}->   {res}anxiety_scale_
  aggressive_scale_1 aggressive_scale_2   {txt}->   {res}aggressive_scale_
        sadness_scale_1 sadness_scale_2   {txt}->   {res}sadness_scale_
      selfconf_scale_1 selfconf_scale_2   {txt}->   {res}selfconf_scale_
        Victimization_1 Victimization_2   {txt}->   {res}Victimization_
                  Conflict_1 Conflict_2   {txt}->   {res}Conflict_
              ID_Ukraine_1 ID_Ukraine_2   {txt}->   {res}ID_Ukraine_
                ID_Russia_1 ID_Russia_2   {txt}->   {res}ID_Russia_
                ID_Europe_1 ID_Europe_2   {txt}->   {res}ID_Europe_
{txt}{hline 77}

{com}. 
. ** Labels survey round variable
. label define waveLB 1 "Round 1 (context primed)" 2 "Round 2 (no context primed)"
{txt}
{com}. label values wave waveLB
{txt}
{com}. 
. *** Sets panelvar to ID_random
. xtset ID_random

{txt}{col 1}Panel variable: {res}ID_random{txt} (balanced)

{com}. 
. 
. ********************************************** MAPPING WARTIME LEADER TRAIT PREFERENCES **********************************************************
. *** Creates Figure 1
. reg Comp_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     1.84
                                                {txt}Prob > F          = {res}    0.1756
                                                {txt}R-squared         = {res}    0.0014
                                                {txt}Root MSE          =    {res} .13531

{txt}{ralign 94:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 Comp_scale_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2} .0101753{col 42}{space 2} .0074981{col 53}{space 1}    1.36{col 62}{space 3}0.176{col 70}{space 4}-.0045685{col 83}{space 3}  .024919
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .8989899{col 42}{space 2} .0071547{col 53}{space 1}  125.65{col 62}{space 3}0.000{col 70}{space 4} .8849212{col 83}{space 3} .9130586
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test _cons == .7034314

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .7034314{p_end}

{txt}       F(  1,   373) ={res}  747.08
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test _cons == .5274064

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .5274064{p_end}

{txt}       F(  1,   373) ={res} 2697.27
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. margins, at(wave=(1 2)) level(95)
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:748}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .8989899{col 26}{space 2} .0071547{col 37}{space 1}  125.65{col 46}{space 3}0.000{col 54}{space 4} .8849212{col 67}{space 3} .9130586
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .9091652{col 26}{space 2} .0068442{col 37}{space 1}  132.84{col 46}{space 3}0.000{col 54}{space 4} .8957071{col 67}{space 3} .9226233
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Competence") ytitle("Competence Importance") title("Competence") legend(off) scheme(s1mono) name(Comp_war_mean_2waves, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:wave}{p_end}
{res}{txt}
{com}. 
. reg Warm_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     8.36
                                                {txt}Prob > F          = {res}    0.0041
                                                {txt}R-squared         = {res}    0.0049
                                                {txt}Root MSE          =    {res} .24422

{txt}{ralign 94:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 Warm_scale_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2}-.0343137{col 42}{space 2}   .01187{col 53}{space 1}   -2.89{col 62}{space 3}0.004{col 70}{space 4}-.0576542{col 83}{space 3}-.0109732
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .7034314{col 42}{space 2} .0118986{col 53}{space 1}   59.12{col 62}{space 3}0.000{col 70}{space 4} .6800347{col 83}{space 3}  .726828
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test _cons == .8989899

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .8989899{p_end}

{txt}       F(  1,   373) ={res}  270.13
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test _cons == .5274064

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .5274064{p_end}

{txt}       F(  1,   373) ={res}  218.86
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. margins, at(wave=(1 2)) level(95)
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:748}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .7034314{col 26}{space 2} .0118986{col 37}{space 1}   59.12{col 46}{space 3}0.000{col 54}{space 4} .6800347{col 67}{space 3}  .726828
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6691176{col 26}{space 2} .0133337{col 37}{space 1}   50.18{col 46}{space 3}0.000{col 54}{space 4}  .642899{col 67}{space 3} .6953363
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Warmth") ytitle("Warmth Importance") title("Warmth") legend(off)  scheme(s1mono) name(Warmth_war_mean_2waves, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:wave}{p_end}
{res}{txt}
{com}. 
. reg Domi_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     0.29
                                                {txt}Prob > F          = {res}    0.5897
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} .26573

{txt}{ralign 94:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 Domi_scale_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2}-.0062389{col 42}{space 2} .0115579{col 53}{space 1}   -0.54{col 62}{space 3}0.590{col 70}{space 4}-.0289657{col 83}{space 3}  .016488
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .5274064{col 42}{space 2} .0134822{col 53}{space 1}   39.12{col 62}{space 3}0.000{col 70}{space 4} .5008958{col 83}{space 3}  .553917
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test _cons == .8989899

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .8989899{p_end}

{txt}       F(  1,   373) ={res}  759.61
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test _cons == .7034314

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .7034314{p_end}

{txt}       F(  1,   373) ={res}  170.46
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. margins, at(wave=(1 2)) level(95)
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:748}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5274064{col 26}{space 2} .0134822{col 37}{space 1}   39.12{col 46}{space 3}0.000{col 54}{space 4} .5008958{col 67}{space 3}  .553917
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5211676{col 26}{space 2} .0140122{col 37}{space 1}   37.19{col 46}{space 3}0.000{col 54}{space 4} .4936148{col 67}{space 3} .5487203
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Dominance") ytitle("Dominance Importance") title("Dominance") legend(off)  scheme(s1mono) name(Domi_war_mean_2waves, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:wave}{p_end}
{res}{txt}
{com}. 
. graph combine Comp_war_mean_2waves Warmth_war_mean_2waves Domi_war_mean_2waves, scheme(s1mono) cols(3)
{res}{txt}
{com}. 
. 
. ************************************************** TESTING THE CONFLICT-SENSITIVITY HYPOTHESIS ***************************************************
. *** Within-respondent test of the conflict-sensitivity hypothesis (testing if trait preferences change across waves for respondents assigned to the peace condition in wave 1)
. * Key results reported in main text and full models in SOM.4b
. reg Comp_scale_ i.wave if include==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       758
                                                {txt}F(1, 378)         =  {res}     2.07
                                                {txt}Prob > F          = {res}    0.1506
                                                {txt}R-squared         = {res}    0.0015
                                                {txt}Root MSE          =    {res} .12291

{txt}{ralign 94:(Std. err. adjusted for {res:379} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 Comp_scale_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2}-.0093814{col 42}{space 2} .0065135{col 53}{space 1}   -1.44{col 62}{space 3}0.151{col 70}{space 4}-.0221887{col 83}{space 3} .0034259
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .9166667{col 42}{space 2} .0058095{col 53}{space 1}  157.79{col 62}{space 3}0.000{col 70}{space 4} .9052437{col 83}{space 3} .9280896
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_ i.wave if include==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       758
                                                {txt}F(1, 378)         =  {res}    28.81
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0167
                                                {txt}Root MSE          =    {res} .22356

{txt}{ralign 94:(Std. err. adjusted for {res:379} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 Warm_scale_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2}-.0582674{col 42}{space 2} .0108552{col 53}{space 1}   -5.37{col 62}{space 3}0.000{col 70}{space 4}-.0796116{col 83}{space 3}-.0369232
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .7365875{col 42}{space 2} .0109741{col 53}{space 1}   67.12{col 62}{space 3}0.000{col 70}{space 4} .7150095{col 83}{space 3} .7581655
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_ i.wave if include==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       758
                                                {txt}F(1, 378)         =  {res}    16.19
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0093
                                                {txt}Root MSE          =    {res} .24655

{txt}{ralign 94:(Std. err. adjusted for {res:379} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 Domi_scale_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2} .0477133{col 42}{space 2} .0118576{col 53}{space 1}    4.02{col 62}{space 3}0.000{col 70}{space 4} .0243982{col 83}{space 3} .0710284
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .4872471{col 42}{space 2} .0127499{col 53}{space 1}   38.22{col 62}{space 3}0.000{col 70}{space 4} .4621776{col 83}{space 3} .5123167
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Testing full interactions between assigned experimental condition (assigned in wave 1) and wave (all respondents assigned to think of the ongoing war in wave 2)
. reg Comp_scale_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,506
                                                {txt}F(3, 752)         =  {res}     1.72
                                                {txt}Prob > F          = {res}    0.1613
                                                {txt}R-squared         = {res}    0.0024
                                                {txt}Root MSE          =    {res} .12921

{txt}{ralign 108:(Std. err. adjusted for {res:753} clusters in {res:ID_random})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                               Comp_scale_{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 38}wave {c |}
{space 14}Round 2 (no context primed)  {c |}{col 44}{res}{space 2}-.0093814{col 56}{space 2} .0065115{col 67}{space 1}   -1.44{col 76}{space 3}0.150{col 84}{space 4}-.0221642{col 97}{space 3} .0034014
{txt}{space 42} {c |}
{space 35}Context {c |}
{space 28}Conflict, now  {c |}{col 44}{res}{space 2}-.0176768{col 56}{space 2} .0092132{col 67}{space 1}   -1.92{col 76}{space 3}0.055{col 84}{space 4}-.0357634{col 97}{space 3} .0004099
{txt}{space 42} {c |}
{space 30}wave#Context {c |}
Round 2 (no context primed)#Conflict, now  {c |}{col 44}{res}{space 2} .0195567{col 56}{space 2} .0099288{col 67}{space 1}    1.97{col 76}{space 3}0.049{col 84}{space 4} .0000652{col 97}{space 3} .0390481
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .9166667{col 56}{space 2} .0058076{col 67}{space 1}  157.84{col 76}{space 3}0.000{col 84}{space 4} .9052656{col 97}{space 3} .9280677
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,506
                                                {txt}F(3, 752)         =  {res}    13.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0124
                                                {txt}Root MSE          =    {res} .23405

{txt}{ralign 108:(Std. err. adjusted for {res:753} clusters in {res:ID_random})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                               Warm_scale_{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 38}wave {c |}
{space 14}Round 2 (no context primed)  {c |}{col 44}{res}{space 2}-.0582674{col 56}{space 2} .0108517{col 67}{space 1}   -5.37{col 76}{space 3}0.000{col 84}{space 4}-.0795707{col 97}{space 3} -.036964
{txt}{space 42} {c |}
{space 35}Context {c |}
{space 28}Conflict, now  {c |}{col 44}{res}{space 2}-.0331561{col 56}{space 2} .0161812{col 67}{space 1}   -2.05{col 76}{space 3}0.041{col 84}{space 4}-.0649219{col 97}{space 3}-.0013904
{txt}{space 42} {c |}
{space 30}wave#Context {c |}
Round 2 (no context primed)#Conflict, now  {c |}{col 44}{res}{space 2} .0239536{col 56}{space 2} .0160798{col 67}{space 1}    1.49{col 76}{space 3}0.137{col 84}{space 4} -.007613{col 97}{space 3} .0555203
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .7365875{col 56}{space 2} .0109706{col 67}{space 1}   67.14{col 76}{space 3}0.000{col 84}{space 4} .7150509{col 97}{space 3} .7581242
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,506
                                                {txt}F(3, 752)         =  {res}     5.69
                                                {txt}Prob > F          = {res}    0.0007
                                                {txt}R-squared         = {res}    0.0051
                                                {txt}Root MSE          =    {res} .25625

{txt}{ralign 108:(Std. err. adjusted for {res:753} clusters in {res:ID_random})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                               Domi_scale_{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 38}wave {c |}
{space 14}Round 2 (no context primed)  {c |}{col 44}{res}{space 2} .0477133{col 56}{space 2} .0118538{col 67}{space 1}    4.03{col 76}{space 3}0.000{col 84}{space 4} .0244428{col 97}{space 3} .0709837
{txt}{space 42} {c |}
{space 35}Context {c |}
{space 28}Conflict, now  {c |}{col 44}{res}{space 2} .0401593{col 56}{space 2} .0185499{col 67}{space 1}    2.16{col 76}{space 3}0.031{col 84}{space 4} .0037435{col 97}{space 3}  .076575
{txt}{space 42} {c |}
{space 30}wave#Context {c |}
Round 2 (no context primed)#Conflict, now  {c |}{col 44}{res}{space 2}-.0539521{col 56}{space 2} .0165531{col 67}{space 1}   -3.26{col 76}{space 3}0.001{col 84}{space 4}-.0864479{col 97}{space 3}-.0214564
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .4872471{col 56}{space 2} .0127458{col 67}{space 1}   38.23{col 76}{space 3}0.000{col 84}{space 4} .4622256{col 97}{space 3} .5122687
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *** SOM.7: Within-respondent test of the Conflict-Sensitivity Hypothesis using factor score variables for trait measurement (reported in table SOM.7.a.2)
. * Testing within-respondent change among respondent assigned to peace condition in wave 1
. reg Comp_PCA_ i.wave if include_PCA==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       718
                                                {txt}F(1, 358)         =  {res}     8.01
                                                {txt}Prob > F          = {res}    0.0049
                                                {txt}R-squared         = {res}    0.0059
                                                {txt}Root MSE          =    {res} .88347

{txt}{ralign 94:(Std. err. adjusted for {res:359} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                   Comp_PCA_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2}-.1362304{col 42}{space 2} .0481478{col 53}{space 1}   -2.83{col 62}{space 3}0.005{col 70}{space 4}-.2309185{col 83}{space 3}-.0415423
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .1545529{col 42}{space 2} .0419426{col 53}{space 1}    3.68{col 62}{space 3}0.000{col 70}{space 4} .0720679{col 83}{space 3} .2370378
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_PCA_ i.wave if include_PCA==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       718
                                                {txt}F(1, 358)         =  {res}     1.10
                                                {txt}Prob > F          = {res}    0.2949
                                                {txt}R-squared         = {res}    0.0007
                                                {txt}Root MSE          =    {res}  .9559

{txt}{ralign 94:(Std. err. adjusted for {res:359} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                   Warm_PCA_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2} -.049396{col 42}{space 2} .0470866{col 53}{space 1}   -1.05{col 62}{space 3}0.295{col 70}{space 4} -.141997{col 83}{space 3}  .043205
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .0740856{col 42}{space 2} .0504443{col 53}{space 1}    1.47{col 62}{space 3}0.143{col 70}{space 4}-.0251188{col 83}{space 3} .1732899
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_PCA_ i.wave if include_PCA==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       718
                                                {txt}F(1, 358)         =  {res}    11.29
                                                {txt}Prob > F          = {res}    0.0009
                                                {txt}R-squared         = {res}    0.0065
                                                {txt}Root MSE          =    {res} .94069

{txt}{ralign 94:(Std. err. adjusted for {res:359} clusters in {res:ID_random})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                   Domi_PCA_{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}wave {c |}
Round 2 (no context primed)  {c |}{col 30}{res}{space 2} .1515254{col 42}{space 2} .0451026{col 53}{space 1}    3.36{col 62}{space 3}0.001{col 70}{space 4} .0628261{col 83}{space 3} .2402248
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-.1184738{col 42}{space 2}  .049912{col 53}{space 1}   -2.37{col 62}{space 3}0.018{col 70}{space 4}-.2166314{col 83}{space 3}-.0203162
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * Full interactions between assigned experimental condition (assigned in wave 1) and wave (all respondents assigned to think of the ongoing war in wave 2)
. reg Comp_PCA_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,452
                                                {txt}F(3, 747)         =  {res}     3.13
                                                {txt}Prob > F          = {res}    0.0250
                                                {txt}R-squared         = {res}    0.0035
                                                {txt}Root MSE          =    {res} .90495

{txt}{ralign 108:(Std. err. adjusted for {res:748} clusters in {res:ID_random})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                                 Comp_PCA_{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 38}wave {c |}
{space 14}Round 2 (no context primed)  {c |}{col 44}{res}{space 2}-.1318403{col 56}{space 2} .0478327{col 67}{space 1}   -2.76{col 76}{space 3}0.006{col 84}{space 4}-.2257428{col 97}{space 3}-.0379379
{txt}{space 42} {c |}
{space 35}Context {c |}
{space 28}Conflict, now  {c |}{col 44}{res}{space 2}-.0949738{col 56}{space 2} .0625124{col 67}{space 1}   -1.52{col 76}{space 3}0.129{col 84}{space 4}-.2176948{col 97}{space 3} .0277472
{txt}{space 42} {c |}
{space 30}wave#Context {c |}
Round 2 (no context primed)#Conflict, now  {c |}{col 44}{res}{space 2} .0984093{col 56}{space 2} .0722142{col 67}{space 1}    1.36{col 76}{space 3}0.173{col 84}{space 4}-.0433577{col 97}{space 3} .2401763
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1445638{col 56}{space 2} .0413564{col 67}{space 1}    3.50{col 76}{space 3}0.001{col 84}{space 4} .0633752{col 97}{space 3} .2257524
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_PCA_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,452
                                                {txt}F(3, 747)         =  {res}     0.98
                                                {txt}Prob > F          = {res}    0.4006
                                                {txt}R-squared         = {res}    0.0020
                                                {txt}Root MSE          =    {res} .98865

{txt}{ralign 108:(Std. err. adjusted for {res:748} clusters in {res:ID_random})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                                 Warm_PCA_{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 38}wave {c |}
{space 14}Round 2 (no context primed)  {c |}{col 44}{res}{space 2}-.0504754{col 56}{space 2} .0466751{col 67}{space 1}   -1.08{col 76}{space 3}0.280{col 84}{space 4}-.1421053{col 97}{space 3} .0411545
{txt}{space 42} {c |}
{space 35}Context {c |}
{space 28}Conflict, now  {c |}{col 44}{res}{space 2}-.1173588{col 56}{space 2} .0729627{col 67}{space 1}   -1.61{col 76}{space 3}0.108{col 84}{space 4}-.2605953{col 97}{space 3} .0258776
{txt}{space 42} {c |}
{space 30}wave#Context {c |}
Round 2 (no context primed)#Conflict, now  {c |}{col 44}{res}{space 2} .0751411{col 56}{space 2} .0694201{col 67}{space 1}    1.08{col 76}{space 3}0.279{col 84}{space 4}-.0611407{col 97}{space 3} .2114229
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .0721408{col 56}{space 2} .0496147{col 67}{space 1}    1.45{col 76}{space 3}0.146{col 84}{space 4}-.0252599{col 97}{space 3} .1695416
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_PCA_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,452
                                                {txt}F(3, 747)         =  {res}     3.86
                                                {txt}Prob > F          = {res}    0.0094
                                                {txt}R-squared         = {res}    0.0039
                                                {txt}Root MSE          =    {res} .99433

{txt}{ralign 108:(Std. err. adjusted for {res:748} clusters in {res:ID_random})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                                 Domi_PCA_{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 38}wave {c |}
{space 14}Round 2 (no context primed)  {c |}{col 44}{res}{space 2} .1238923{col 56}{space 2} .0457027{col 67}{space 1}    2.71{col 76}{space 3}0.007{col 84}{space 4} .0341712{col 97}{space 3} .2136133
{txt}{space 42} {c |}
{space 35}Context {c |}
{space 28}Conflict, now  {c |}{col 44}{res}{space 2} .1682441{col 56}{space 2} .0734209{col 67}{space 1}    2.29{col 76}{space 3}0.022{col 84}{space 4} .0241083{col 97}{space 3} .3123799
{txt}{space 42} {c |}
{space 30}wave#Context {c |}
Round 2 (no context primed)#Conflict, now  {c |}{col 44}{res}{space 2}-.2086936{col 56}{space 2}   .06582{col 67}{space 1}   -3.17{col 76}{space 3}0.002{col 84}{space 4}-.3379079{col 97}{space 3}-.0794794
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}-.1018748{col 56}{space 2} .0493121{col 67}{space 1}   -2.07{col 76}{space 3}0.039{col 84}{space 4}-.1986816{col 97}{space 3} -.005068
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *******************************************************************************************************************************************************
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\au206393\OneDrive - Aarhus universitet\Desktop\PSRM acceptance log-file\trait_preferences_Ukraine.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 May 2025, 14:12:01
{txt}{.-}
{smcl}
{txt}{sf}{ul off}